research-article

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Authors:

Muneeb Ul Hassan,

Mubashir Husain Rehmani,

Jinjun ChenAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 22, Issue 1

Pages 746 - 789

https://doi.org/10.1109/COMST.2019.2944748

Published: 01 January 2020 Publication History

Abstract

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

References

[1]

C. Yu, S. Jing, and X. Li, “An architecture of cyber physical system based on service,” in Proc. IEEE Int. Conf. Comput. Sci. Service Syst. (CSSS), Nanjing, China, 2012, pp. 1409–1412.

[2]

E. A. Lee, “Cyber physical systems: Design challenges,” in Proc. 11th IEEE Symp. Object Orient. Real Time Distrib. Comput. (ISORC), Orlando, FL, USA, 2008, pp. 363–369.

[3]

T. Zhu, P. Xiong, G. Li, W. Zhou, and P. S. Yu, “Differentially private model publishing in cyber physical systems,” Future Gener. Comput. Syst., to be published.

[4]

J. Shi, J. Wan, H. Yan, and H. Suo, “A survey of cyber-physical systems,” in Proc. IEEE Int. Conf. Wireless Commun. Signal Process. (WCSP), Nanjing, China, 2011, pp. 1–6.

[5]

J. Giraldo, E. Sarkar, A. A. Cardenas, M. Maniatakos, and M. Kantarcioglu, “Security and privacy in cyber-physical systems: A survey of surveys,” IEEE Des. Test., vol. 34, no. 4, pp. 7–17, Aug. 2017.

[6]

Q. Xu, P. Ren, H. Song, and Q. Du, “Security-aware waveforms for enhancing wireless communications privacy in cyber-physical systems via multipath receptions,” IEEE Internet Things J., vol. 4, no. 6, pp. 1924–1933, Dec. 2017.

[7]

M. Gowtham and S. S. Ahila, “Privacy enhanced data communication protocol for wireless body area network,” in Proc. 4th IEEE Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), 2017, pp. 1–5.

[8]

M. Li, W. Lou, and K. Ren, “Data security and privacy in wireless body area networks,” IEEE Wireless Commun., vol. 17, no. 1, pp. 51–58, Feb. 2010.

Digital Library

[9]

Z. Wang, H. Chen, Q. Cao, H. Qi, Z. Wang, and Q. Wang, “Achieving location error tolerant barrier coverage for wireless sensor networks,” Comput. Netw., vol. 112, pp. 314–328, Jan. 2017.

Digital Library

[10]

P. Barbosa, A. Brito, and H. Almeida, “A technique to provide differential privacy for appliance usage in smart metering,” Inf. Sci., vols. 370–371, pp. 355–367, Nov. 2016.

Digital Library

[11]

T. Wang, Z. Zheng, M. H. Rehmani, S. Yao, and Z. Huo, “Privacy preservation in big data from the communication perspective—A survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 1, pp. 753–778, 1st Quart., 2019.

[12]

Y. Shen and H. Jin, “Privacy-preserving personalized recommendation: An instance-based approach via differential privacy,” in Proc. IEEE Int. Conf. Data Min. (ICDM), Shenzhen, China, 2014, pp. 540–549.

[13]

L. Chenet al., “Robustness, security and privacy in location-based services for future IoT: A survey,” IEEE Access, vol. 5, pp. 8956–8977, 2017.

[14]

K. Muhammad, R. Hamza, J. Ahmad, J. Lloret, H. H. G. Wang, and S. W. Baik, “Secure surveillance framework for IoT systems using probabilistic image encryption,” IEEE Trans. Ind. Informat., vol. 14, no. 8, pp. 3679–3689, Aug. 2018.

[15]

W. Meng, E. W. Tischhauser, Q. Wang, Y. Wang, and J. Han, “When intrusion detection meets blockchain technology: A review,” IEEE Access, vol. 6, pp. 10179–10188, 2018.

[16]

L. Sweeney, “k-anonymity: A model for protecting privacy,” Int. J. Uncertainty Fuzziness Knowl. Based Syst., vol. 10, no. 5, pp. 557–570, 2002.

Digital Library

[17]

C. Dwork, “A firm foundation for private data analysis,” Commun. ACM, vol. 54, no. 1, pp. 86–95, 2011.

Digital Library

[18]

R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao, “Toward efficient and privacy-preserving computing in big data era,” IEEE Netw., vol. 28, no. 4, pp. 46–50, Jul./Aug. 2014.

[19]

X. Yang, T. Wang, X. Ren, and W. Yu, “Survey on improving data utility in differentially private sequential data publishing,” IEEE Trans. Big Data, to be published.

[20]

Y. Cao, M. Yoshikawa, Y. Xiao, and L. Xiong, “Quantifying differential privacy in continuous data release under temporal correlations,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 7, pp. 1281–1295, Jul. 2019.

[21]

K.-A. Shim, “A survey of public-key cryptographic primitives in wireless sensor networks,” IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 577–601, 1st Quart., 2016.

Digital Library

[22]

K. Apostol, Brute-force Attack. 2012.

Digital Library

[23]

M. E. Skarkala, M. Maragoudakis, S. Gritzalis, L. Mitrou, H. Toivonen, and P. Moen, “Privacy preservation by k-anonymization of weighted social networks,” in Proc. IEEE Comput. Soc. Int. Conf. Adv. Soc. Netw. Anal. Min. (ASONAM), 2012, pp. 423–428.

[24]

Y.-A. De Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel, “Unique in the crowd: The privacy bounds of human mobility,” Sci. Rep., vol. 3, p. 1376, Mar. 2013.

[25]

Y.-A. De Montjoye, L. Radaelli, V. K. Singh, and A. Pentland, “Unique in the shopping mall: On the reidentifiability of credit card metadata,” Science, vol. 347, no. 6221, pp. 536–539, 2015.

[26]

C. Dwork, “Differential privacy,” in Proc. 33rd Int. Conf. Automata Lang. Program. (ICALP) Vol. II, 2006, pp. 1–12.

[27]

L. Wasserman and S. Zhou, “A statistical framework for differential privacy,” J. Amer. Stat. Assoc., vol. 105, no. 489, pp. 375–389, 2010.

[28]

N. Li, W. Qardaji, D. Su, Y. Wu, and W. Yang, “Membership privacy: A unifying framework for privacy definitions,” in Proc. ACM SIGSAC Conf. Comput. Commun. Security, Berlin, Germany, 2013, pp. 889–900.

[29]

J. Lee and C. Clifton, “Differential identifiability,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Beijing, China, 2012, pp. 1041–1049.

[30]

T. Zhu, G. Li, W. Zhou, and P. S. Yu, Preliminary of Differential Privacy. Cham, Switzerland: Springer Int., 2017, pp. 7–16. [Online]. Available: https://doi.org/10.1007/978-3-319-62004-6_2

[31]

H. Zhang, Y. Shu, P. Cheng, and J. Chen, “Privacy and performance trade-off in cyber-physical systems,” IEEE Netw., vol. 30, no. 2, pp. 62–66, Mar./Apr. 2016.

Digital Library

[32]

E. Zheleva and L. Getoor, “Privacy in social networks: A survey,” in Social Network Data Analytics. Boston, MA, USA: Springer, 2011, pp. 277–306.

[33]

C. Task and C. Clifton, “What should we protect? Defining differential privacy for social network analysis,” in State of the Art Applications of Social Network Analysis. Cham, Switzerland: Springer, 2014, pp. 139–161.

[34]

I. Gazeau, D. Miller, and C. Palamidessi, “Preserving differential privacy under finite-precision semantics,” Theor. Comput. Sci., vol. 655, pp. 92–108, Dec. 2016.

Digital Library

[35]

Z. Ji, Z. C. Lipton, and C. Elkan, “Differential privacy and machine learning: A survey and review,” arXiv preprint arXiv:1412.7584, 2014.

[36]

K. Xu and Z. Yan, “Privacy protection in mobile recommender systems: A survey,” in Proc. Int. Conf. Security Privacy Anonymity Comput. Commun. Stor., 2016, pp. 305–318.

[37]

C. Dwork, “Differential privacy: A survey of results,” in Proc. Int. Conf. Theory Appl. Models Comput., 2008, pp. 1–19.

[38]

K. Ligett and A. Roth, “Take it or leave it: Running a survey when privacy comes at a cost,” in Proc. Int. Workshop Internet Netw. Econ., 2012, pp. 378–391.

[39]

S. Vadhan, “The complexity of differential privacy,” in Tutorials on the Foundations of Cryptography. Cham, Switzerland: Springer, 2017, pp. 347–450.

[40]

X. Yao, X. Zhou, and J. Ma, “Differential privacy of big data: An overview,” in Proc. IEEE 2nd Int. Conf. Big Data Security Cloud (BigDataSecurity), New York, NY, USA, 2016, pp. 7–12.

[41]

S. Yu, “Big privacy: Challenges and opportunities of privacy study in the age of big data,” IEEE Access, vol. 4, pp. 2751–2763, 2016.

[42]

T. Zhu, G. Li, W. Zhou, and P. S. Yu, “Differentially private data publishing and analysis: A survey,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 8, pp. 1619–1638, Aug. 2017.

Digital Library

[43]

S. H. Begum and F. Nausheen, “A comparative analysis of differential privacy vs other privacy mechanisms for big data,” in Proc. IEEE 2nd Int. Conf. Inventive Syst. Control (ICISC), 2018, pp. 512–516.

[44]

P. Jain, M. Gyanchandani, and N. Khare, “Differential privacy: Its technological prescriptive using big data,” J. Big Data, vol. 5, no. 1, p. 15, 2018.

[45]

J. Zhao, Y. Chen, and W. Zhang, “Differential privacy preservation in deep learning: Challenges, opportunities and solutions,” IEEE Access, vol. 7, pp. 48901–48911, 2019.

[46]

D. Desfontaines and B. Pejó, “SoK: Differential privacies,” arXiv preprint arXiv:1906.01337, 2019.

[47]

D. Lv and S. Zhu, “Achieving correlated differential privacy of big data publication,” Comput. Security, vol. 82, pp. 184–195, May 2019.

[48]

D. Agrawal and D. Kesdogan, “Measuring anonymity: The disclosure attack,” IEEE Security Privacy, vol. 1, no. 6, pp. 27–34, Nov./Dec. 2003.

Digital Library

[49]

R. Boussada, M. E. Elhdhili, and L. A. Saidane, “A survey on privacy: Terminology, mechanisms and attacks,” in Proc. IEEE/ACS 13th Int. Conf. Comput. Syst. Appl. (AICCSA), Agadir, Morocco, 2016, pp. 1–7.

[50]

S. Gambs, M.-O. Killijian, and M. N. del Prado Cortez, “De-anonymization attack on geolocated data,” J. Comput. Syst. Sci., vol. 80, no. 8, pp. 1597–1614, 2014.

Digital Library

[51]

G. Danezis, “Statistical disclosure attacks,” in Proc. IFIP Int. Inf. Security Conf., 2003, pp. 421–426.

[52]

X. Zhou, S. D. Wolthusen, C. Busch, and A. Kuijper, “Feature correlation attack on biometric privacy protection schemes,” in Proc. IEEE 5th Int. Conf. Intell. Inf. Hiding Multimedia Signal Process. (IIH-MSP), Kyoto, Japan, 2009, pp. 1061–1065.

[53]

F. Li, B. Luo, P. Liu, A. C. Squicciarini, D. Lee, and C.-H. Chu, “Defending against attribute-correlation attacks in privacy-aware information brokering,” in Proc. Int. Conf. Collaborative Comput. Netw. Appl. Worksharing, 2008, pp. 100–112.

[54]

B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: A survey of recent developments,” ACM Comput. Surveys, vol. 42, no. 4, pp. 1–53, Jun. 2010.

Digital Library

[55]

F. McSherry and K. Talwar, “Mechanism design via differential privacy,” in Proc. 48th Annu. IEEE Symp. Found. Comput. Sci. (FOCS), Providence, RI, USA, 2007, pp. 94–103.

[56]

F. Liu, “Generalized Gaussian mechanism for differential privacy,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 4, pp. 747–756, Apr. 2019.

Digital Library

[57]

Q. Geng and P. Viswanath, “The optimal noise-adding mechanism in differential privacy,” IEEE Trans. Inf. Theory, vol. 62, no. 2, pp. 925–951, Feb. 2016.

Digital Library

[58]

F. Eigner, A. Kate, M. Maffei, F. Pampaloni, and I. Pryvalov, “Differentially private data aggregation with optimal utility,” in Proc. 30th ACM Annu. Comput. Security Appl. Conf., New Orleans, LA, USA, 2014, pp. 316–325.

[59]

Q. Geng and P. Viswanath, “The optimal mechanism in differential privacy,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Honolulu, HI, USA, 2014, pp. 2371–2375.

[60]

J. Soria-Comas and J. Domingo-Ferrer, “Optimal data-independent noise for differential privacy,” Inf. Sci., vol. 250, pp. 200–214, Nov. 2013.

[61]

A. Ghosh, T. Roughgarden, and M. Sundararajan, “Universally utility-maximizing privacy mechanisms,” SIAM J. Comput., vol. 41, no. 6, pp. 1673–1693, 2012.

Digital Library

[62]

M. Gupte and M. Sundararajan, “Universally optimal privacy mechanisms for minimax agents,” in Proc. 29th ACM SIGMOD-SIGACT-SIGART Symp. Principles Database Syst., Indianapolis, IN, USA, 2010, pp. 135–146.

[63]

T. Zhu, P. Xiong, G. Li, and W. Zhou, “Correlated differential privacy: Hiding information in non-IID data set,” IEEE Trans. Inf. Forensics Security, vol. 10, no. 2, pp. 229–242, Feb. 2015.

[64]

Y. Xiao and L. Xiong, “Protecting locations with differential privacy under temporal correlations,” in Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Security, Denver, CO, USA, 2015, pp. 1298–1309.

[65]

G. Kellaris and S. Papadopoulos, “Practical differential privacy via grouping and smoothing,” Proc. VLDB Endow., vol. 6, no. 5, pp. 301–312, 2013.

Digital Library

[66]

X. Xiao, G. Bender, M. Hay, and J. Gehrke, “iReduct: Differential privacy with reduced relative errors,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, Athens, Greece, 2011, pp. 229–240.

[67]

K. Nissim, S. Raskhodnikova, and A. Smith, “Smooth sensitivity and sampling in private data analysis,” in Proc. 39th Annu. ACM Symp. Theory Comput., San Diego, CA, USA, 2007, pp. 75–84.

[68]

W.-Y. Day and N. Li, “Differentially private publishing of high-dimensional data using sensitivity control,” in Proc. 10th ACM Symp. Inf. Comput. Commun. Security, 2015, pp. 451–462.

[69]

A. Inan, M. E. Gursoy, and Y. Saygin, “Sensitivity analysis for non-interactive differential privacy: Bounds and efficient algorithms,” IEEE Trans. Depend. Secure Comput., to be published.

[70]

G. Cormode, C. Procopiuc, D. Srivastava, E. Shen, and T. Yu, “Differentially private spatial decompositions,” in Proc. IEEE 28th Int. Conf. Data Eng. (ICDE), 2012, pp. 20–31.

[71]

G. Acs, C. Castelluccia, and R. Chen, “Differentially private histogram publishing through lossy compression,” in Proc. IEEE 12th Int. Conf. Data Min., Brussels, Belgium, 2012, pp. 1–10.

[72]

X. Xiao, G. Wang, and J. Gehrke, “Differential privacy via wavelet transforms,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 8, pp. 1200–1214, Aug. 2011.

Digital Library

[73]

Y. D. Li, Z. Zhang, M. Winslett, and Y. Yang, “Compressive mechanism: Utilizing sparse representation in differential privacy,” in Proc. 10th Annu. ACM Workshop Privacy Electron. Soc., Chicago, IL, USA, 2011, pp. 177–182.

[74]

V. Rastogi and S. Nath, “Differentially private aggregation of distributed time-series with transformation and encryption,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, Indianapolis, IN, USA, 2010, pp. 735–746.

[75]

S. Papadimitriou, F. Li, G. Kollios, and P. S. Yu, “Time series compressibility and privacy,” in Proc. 33rd Int. Conf. Very Large Data Bases, Vienna, Austria, 2007, pp. 459–470.

[76]

X. Yang, X. Ren, J. Lin, and W. Yu, “On binary decomposition based privacy-preserving aggregation schemes in real-time monitoring systems,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 10, pp. 2967–2983, Oct. 2016.

Digital Library

[77]

J. Hua, Y. Gao, and S. Zhong, “Differentially private publication of general time-serial trajectory data,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), 2015, pp. 549–557.

[78]

W. Qardaji, W. Yang, and N. Li, “Differentially private grids for geospatial data,” in Proc. IEEE 29th Int. Conf. Data Eng. (ICDE), Brisbane, QLD, Australia, 2013, pp. 757–768.

[79]

R. Chen, N. Mohammed, B. C. Fung, B. C. Desai, and L. Xiong, “Publishing set-valued data via differential privacy,” Proc. VLDB Endow., vol. 4, no. 11, pp. 1087–1098, 2011.

Digital Library

[80]

R. Chen, B. C. M. Fung, B. C. Desai, and N. M. Sossou, “Differentially private transit data publication: A case study on the montreal transportation system,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., 2012, pp. 213–221.

[81]

R. Chen, G. Acs, and C. Castelluccia, “Differentially private sequential data publication via variable-length n-grams,” in Proc. ACM Conf. Comput. Commun. Security, Raleigh, NC, USA, 2012, pp. 638–649.

[82]

H. Li, L. Xiong, X. Jiang, and J. Liu, “Differentially private histogram publication for dynamic datasets: An adaptive sampling approach,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manag., Melbourne, VIC, Australia, 2015, pp. 1001–1010.

[83]

L. Fan and L. Xiong, “An adaptive approach to real-time aggregate monitoring with differential privacy,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 9, pp. 2094–2106, Sep. 2014.

[84]

L. Fan, L. Bonomi, L. Xiong, and V. Sunderam, “Monitoring Web browsing behavior with differential privacy,” in ACM Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 177–188.

[85]

C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Found. Trends^® Theor. Comput. Sci., vol. 9, nos. 3–4, pp. 211–407, 2014.

Digital Library

[86]

B. Balle and Y.-X. Wang, “Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising,” arXiv preprint arXiv:1805.06530, 2018.

[87]

A. Machanavajjhala, X. He, and M. Hay, “Differential privacy in the wild: A tutorial on current practices & open challenges,” in Proc. ACM Int. Conf. Manag. Data, 2017, pp. 1727–1730.

[88]

Z. Lu and H. Shen, “A new lower bound of privacy budget for distributed differential privacy,” in Proc. 18th Int. Conf. Parallel Distrib. Comput. Appl. Technol. (PDCAT), 2017, pp. 25–32.

[89]

F. D. McSherry, “Privacy integrated queries: An extensible platform for privacy-preserving data analysis,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, Providence, RI, USA, 2009, pp. 19–30.

[90]

J. Soria-Comas, J. Domingo-Ferrer, D. Sánchez, and D. Megías, “Individual differential privacy: A utility-preserving formulation of differential privacy guarantees,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 6, pp. 1418–1429, Jun. 2017.

Digital Library

[91]

E. ElSalamouny and S. Gambs, “Differential privacy models for location-based services,” Trans. Data Privacy, vol. 9, no. 1, pp. 15–48, 2016.

Digital Library

[92]

X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava, “DPT: Differentially private trajectory synthesis using hierarchical reference systems,” Proc. VLDB Endow., vol. 8, no. 11, pp. 1154–1165, 2015.

Digital Library

[93]

Q. Xiao, R. Chen, and K.-L. Tan, “Differentially private network data release via structural inference,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., New York, NY, USA, 2014, pp. 911–920.

[94]

J. Hsuet al., “Differential privacy: An economic method for choosing epsilon,” in Proc. IEEE 27th Comput. Security Found. Symp. (CSF), 2014, pp. 398–410.

[95]

J. Lee and C. Clifton, “How much is enough? Choosing $\varepsilon$ for differential privacy,” in Proc. Int. Conf. Inf. Security, 2011, pp. 325–340.

[96]

L. K. Fleischer and Y.-H. Lyu, “Approximately optimal auctions for selling privacy when costs are correlated with data,” in Proc. 13th ACM Conf. Electron. Commerce, Valencia, Spain, 2012, pp. 568–585.

[97]

P. Dandekar, N. Fawaz, and S. Ioannidis, “Privacy auctions for recommender systems,” ACM Trans. Econ. Comput., vol. 2, no. 3, p. 12, 2014.

[98]

C. Li, D. Y. Li, G. Miklau, and D. Suciu, “A theory of pricing private data,” ACM Trans. Database Syst., vol. 39, no. 4, p. 34, 2014.

[99]

C. Han and K. Wang, “Sensitive disclosures under differential privacy guarantees,” in Proc. IEEE Int. Congr. Big Data (BigData Congr.), New York, NY, USA, 2015, pp. 110–117.

[100]

E. Shen and T. Yu, “Mining frequent graph patterns with differential privacy,” in Proc. 19th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Chicago, IL, USA, 2013, pp. 545–553.

[101]

B. Yang, I. Sato, and H. Nakagawa, “Bayesian differential privacy on correlated data,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, Melbourne, VIC, Australia, 2015, pp. 747–762.

[102]

X. Xiao, “Differentially private data release: Improving utility with wavelets and Bayesian networks,” in Proc. Asia–Pac. Web Conf., 2014, pp. 25–35.

[103]

W. Jiang, C. Xie, and Z. Zhang, “Wishart mechanism for differentially private principal components analysis,” in Proc. AAAI, 2016, pp. 1730–1736.

[104]

C. Dwork, K. Talwar, A. Thakurta, and L. Zhang, “Analyze gauss: optimal bounds for privacy-preserving principal component analysis,” in Proc. 46th Annu. ACM Symp. Theory Comput., New York, NY, USA, 2014, pp. 11–20.

[105]

G. Barthe and B. Kopf, “Information-theoretic bounds for differentially private mechanisms,” in Proc. 24th IEEE Comput. Security Found. Symp., 2011, pp. 191–204.

[106]

G. Giaconi, D. Gündüz, and H. V. Poor, “Smart meter privacy with renewable energy and an energy storage device,” IEEE Trans. Inf. Forensics Security, vol. 13, no. 1, pp. 129–142, Jan. 2018.

[107]

N. Zhang and W. Zhao, “Privacy-preserving OLAP: An information-theoretic approach,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 1, pp. 122–138, Jan. 2011.

Digital Library

[108]

M. S. Alvim, K. Chatzikokolakis, P. Degano, and C. Palamidessi, “Differential privacy versus quantitative information flow,” arXiv preprint arXiv:1012.4250, 2010.

[109]

L. Sankar, S. R. Rajagopalan, S. Mohajer, and H. V. Poor, “Smart meter privacy: A theoretical framework,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 837–846, Jun. 2013.

[110]

S. Li, A. Khisti, and A. Mahajan, “Information-theoretic privacy for smart metering systems with a rechargeable battery,” IEEE Trans. Inf. Theory, vol. 64, no. 5, pp. 3679–3695, May 2018.

[111]

P. D. Beale and R. K. Pathria, Statistical Mechanics. Amsterdam, The Netherlands: Elsevier, 2011.

[112]

C. Cachin, “Entropy measures and unconditional security in cryptography,” Ph.D. dissertation, ETH Zurich, Zürich, Switzerland, 1997.

[113]

S. Asoodeh, F. Alajaji, and T. Linder, “Notes on information-theoretic privacy,” in Proc. IEEE 52nd Annu. Allerton Conf. Commun. Control Comput. (Allerton), Monticello, IL, USA, 2014, pp. 1272–1278.

[114]

L. Sankar, S. R. Rajagopalan, and H. V. Poor, “An information-theoretic approach to privacy,” in Proc. IEEE 48th Annu. Allerton Conf. Commun. Control Comput. (Allerton), 2010, pp. 1220–1227.

[115]

M. Bezzi, “An information theoretic approach for privacy metrics,” Trans. Data Privacy, vol. 3, no. 3, pp. 199–215, 2010.

Digital Library

[116]

J. Eckert and J. Mauchly. (1946). Outline of Plans for Development of Electronic Computers. [Online]. Available: http://archive.computerhistory.org/resources/access/text/2010/08/102660910-05-01-acc

[117]

C. L. Liu and J. W. Layland, “Scheduling algorithms for multiprogramming in a hard-real-time environment,” J. ACM, vol. 20, no. 1, pp. 46–61, 1973.

Digital Library

[118]

K.-D. Kim and P. R. Kumar, “Cyber-physical systems: A perspective at the centennial,” Proc. IEEE, vol. 100, pp. 1287–1308, May 2012.

[119]

B. M. Leineret al., “A brief history of the Internet,” ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 5, pp. 22–31, 2009.

Digital Library

[120]

J. M. Kahn, R. H. Katz, and K. S. J. Pister, “Next century challenges: Mobile networking for ‘smart dust,”’ in Proc. 5th Annu. ACM/IEEE Int. Conf. Mobile Comput. Netw., Seattle, WA, USA, 1999, pp. 271–278.

[121]

G.-G. Wang, X. Cai, Z. Cui, G. Min, and J. Chen, “High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm,” IEEE Trans. Emerg. Topics Comput., to be published.

[122]

Z. Cui, B. Sun, G. Wang, Y. Xue, and J. Chen, “A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems,” J. Parallel Distrib. Comput., vol. 103, pp. 42–52, May 2017.

Digital Library

[123]

J. Lee, B. Bagheri, and H.-A. Kao, “A cyber-physical systems architecture for industry 4.0-based manufacturing systems,” Manuf. Lett., vol. 3, pp. 18–23, Jan. 2015.

[124]

X.-M. Zhang, Q.-L. Han, and Y.-L. Wang, “A brief survey of recent results on control and filtering for networked systems,” in Proc. 12th IEEE World Congr. Intell. Control Autom. (WCICA), Guilin, China, 2016, pp. 64–69.

[125]

P. Tabuada, “Event-triggered real-time scheduling of stabilizing control tasks,” IEEE Trans. Autom. Control, vol. 52, no. 9, pp. 1680–1685, Sep. 2007.

[126]

J. Lunze and D. Lehmann, “A state-feedback approach to event-based control,” Automatica, vol. 46, no. 1, pp. 211–215, 2010.

Digital Library

[127]

X. Wang and M. D. Lemmon, “Self-triggered feedback control systems with finite-gain L2 stability,” IEEE Trans. Autom. Control, vol. 54, no. 3, pp. 452–467, Mar. 2009.

[128]

J. P. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent results in networked control systems,” Proc. IEEE, vol. 95, no. 1, pp. 138–162, Jan. 2007.

[129]

J. Xiong and J. Lam, “Stabilization of networked control systems with a logic ZOH,” IEEE Trans. Autom. Control, vol. 54, no. 2, pp. 358–363, Feb. 2009.

[130]

D. Nesic and D. Liberzon, “A unified framework for design and analysis of networked and quantized control systems,” IEEE Trans. Autom. Control, vol. 54, no. 4, pp. 732–747, Apr. 2009.

[131]

N. Lynch, R. Segala, F. Vaandrager, and H. B. Weinberg, “Hybrid I/O automata,” in Proc. Int. Hybrid Syst. Workshop, 1995, pp. 496–510.

[132]

A. Platzer, Logical Analysis of Hybrid Systems: Proving Theorems for Complex Dynamics. Heidelberg, Germany: Springer, 2010.

[133]

M. H. Rehmani, M. Reisslein, A. Rachedi, M. Erol-Kantarci, and M. Radenkovic, “Integrating renewable energy resources into the smart grid: Recent developments in information and communication technologies,” IEEE Trans. Ind. Informat., vol. 14, no. 7, pp. 2814–2825, Jul. 2018.

[134]

M. H. Cintuglu, O. A. Mohammed, K. Akkaya, and A. S. Uluagac, “A survey on smart grid cyber-physical system testbeds,” IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 446–464, 1st Quart., 2017.

Digital Library

[135]

Z. MacHardy, A. Khan, K. Obana, and S. Iwashina, “V2X access technologies: Regulation, research, and remaining challenges,” IEEE Commun. Surveys Tuts., vol. 20, no. 3, pp. 1858–1877, 3rd Quart., 2018.

[136]

L. Da Xu, W. He, and S. Li, “Internet of Things in industries: A survey,” IEEE Trans. Ind. Informat., vol. 10, no. 4, pp. 2233–2243, Nov. 2014.

[137]

Z. Cuiet al., “A pigeon-inspired optimization algorithm for many-objective optimization problems,” Sci. China Inf. Sci., vol. 62, no. 7, Jan. 2019, Art. no. [Online]. Available: https://doi.org/10.1007/s11432-018-9729-5

[138]

G. Bloom, B. Alsulami, E. Nwafor, and I. C. Bertolotti, “Design patterns for the Industrial Internet of Things,” in Proc. 14th IEEE Int. Workshop Factory Commun. Syst. (WFCS), 2018, pp. 1–10.

[139]

X. Huet al., “Differential privacy in telco big data platform,” Proc. VLDB Endow., vol. 8, no. 12, pp. 1692–1703, 2015.

Digital Library

[140]

H. Ye, J. Liu, W. Wang, P. Li, T. Li, and J. Li, “Secure and efficient outsourcing differential privacy data release scheme in cyber–physical system,” Future Gener. Comput. Syst., to be published.

[141]

M. Naehrig, K. Lauter, and V. Vaikuntanathan, “Can homomorphic encryption be practical?” in Proc. 3rd ACM Workshop Cloud Comput. Security, 2011, pp. 113–124.

[142]

P. Jain, M. Gyanchandani, and N. Khare, “Big data privacy: A technological perspective and review,” J. Big Data, vol. 3, no. 1, p. 25, 2016.

[143]

A. Gosain and N. Chugh, “Privacy preservation in big data,” Int. J. Comput. Appl., vol. 100, no. 17, pp. 44–47, 2014.

[144]

A. S. Thanamani, “Comparison and analysis of anonymization techniques for preserving privacy in big data,” Adv. Comput. Sci. Technol., vol. 10, no. 2, pp. 247–253, 2017.

[145]

R. Chen, B. C. M. Fung, P. S. Yu, and B. C. Desai, “Correlated network data publication via differential privacy,” Int. J. Very Large Data Bases (VLDB), vol. 23, no. 4, pp. 653–676, 2014.

Digital Library

[146]

R. Lu, K. Heung, A. H. Lashkari, and A. A. Ghorbani, “A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT,” IEEE Access, vol. 5, pp. 3302–3312, 2017.

[147]

C. Xu, J. Ren, D. Zhang, and Y. Zhang, “Distilling at the edge: A local differential privacy obfuscation framework for IoT data analytics,” IEEE Commun. Mag., vol. 56, no. 8, pp. 20–25, Aug. 2018.

Digital Library

[148]

G. Ács and C. Castelluccia, “I have a DREAM! (Differentially private smart metering),” in Proc. Int. Workshop Inf. Hiding, 2011, pp. 118–132.

[149]

F. Kargl, A. Friedman, and R. Boreli, “Differential privacy in intelligent transportation systems,” in Proc. 6th ACM Conf. Security Privacy Wireless Mobile Netw., 2013, pp. 107–112.

[150]

J. Zhao, T. Jung, Y. Wang, and X. Li, “Achieving differential privacy of data disclosure in the smart grid,” in Proc. IEEE INFOCOM, 2014, pp. 504–512.

[151]

H. Li, Y. Dai, and X. Lin, “Efficient e-health data release with consistency guarantee under differential privacy,” in Proc. IEEE 17th Int. Conf. E-Health Netw. Appl. Services (HealthCom), 2015, pp. 602–608.

[152]

N. Mohammed, S. Barouti, D. Alhadidi, and R. Chen, “Secure and private management of healthcare databases for data mining,” in Proc. IEEE 28th Int. Symp. Comput. Based Med. Syst. (CBMS), 2015, pp. 191–196.

[153]

S. Han, U. Topcu, and G. J. Pappas, “An approximately truthful mechanism for electric vehicle charging via joint differential privacy,” in Proc. IEEE Amer. Control Conf. (ACC), 2015, pp. 2469–2475.

[154]

M. Savi, C. Rottondi, and G. Verticale, “Evaluation of the precision-privacy tradeoff of data perturbation for smart metering,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2409–2416, Sep. 2015.

[155]

C. R. G. Rodríguez and S. E. G. Barrantes, “Using differential privacy for the Internet of Things,” in IFIP International Summer School on Privacy and Identity Management. Cham, Switzerland: Springer, 2016, pp. 201–211.

[156]

G. Eibl and D. Engel, “Differential privacy for real smart metering data,” Comput. Sci. Res. Develop., vol. 32, nos. 1–2, pp. 173–182, 2017.

Digital Library

[157]

H. Zhai, S. Chen, and D. An, “ExPO: Exponential-based privacy preserving online auction for electric vehicles demand response in microgrid,” in Proc. IEEE 13th Int. Conf. Semantics Knowl. Grids (SKG), 2017, pp. 126–131.

[158]

Y. Shi, C. Piao, and L. Zheng, “Differential-privacy-based correlation analysis in railway freight service applications,” in Proc. IEEE Int. Conf. Cyber Enabled Distrib. Comput. Knowl. Disc. (CyberC), 2017, pp. 35–39.

[159]

J. Zhang, X. Liang, Z. Zhang, S. He, and Z. Shi, “Re-DPoctor: Real-time health data releasing with w-day differential privacy,” arXiv preprint arXiv:1711.00232, 2017.

[160]

Y. Wang, Z. Huang, S. Mitra, and G. E. Dullerud, “Differential privacy in linear distributed control systems: Entropy minimizing mechanisms and performance tradeoffs,” IEEE Trans. Control Netw. Syst., vol. 4, no. 1, pp. 118–130, Mar. 2017.

[161]

H. Cao, S. Liu, L. Wu, Z. Guan, and X. Du, “Achieving differential privacy against non-intrusive load monitoring in smart grid: A fog computing approach,” in Concurrency and Computation: Practice and Experience, Wiley, 2018, Art. no.

[162]

T. Zhang and Q. Zhu, “Distributed privacy-preserving collaborative intrusion detection systems for VANETs,” IEEE Trans. Signal Inf. Process. Netw., vol. 4, no. 1, pp. 148–161, Mar. 2018.

[163]

J. L. Raisaroet al., “MedCo: Enabling secure and privacy-preserving exploration of distributed clinical and genomic data,” IEEE/ACM Trans. Comput. Biol. Bioinformat., vol. 16, no. 4, pp. 1328–1341, Jul./Aug. 2019.

Digital Library

[164]

L. Ni, C. Li, H. Liu, A. G. Bourgeois, and J. Yu, “Differential private preservation multi-core DBScan clustering for network user data,” Procedia Comput. Sci., vol. 129, pp. 257–262, 2018.

[165]

Z. Zhang, Z. Qin, L. Zhu, J. Weng, and K. Ren, “Cost-friendly differential privacy for smart meters: Exploiting the dual roles of the noise,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 619–626, Mar. 2017.

[166]

D. Kifer and A. Machanavajjhala, “Pufferfish: A framework for mathematical privacy definitions,” ACM Trans. Database Syst., vol. 39, no. 1, p. 3, 2014.

Digital Library

[167]

F. K. Dankar and K. El Emam, “Practicing differential privacy in health care: A review,” Trans. Data Privacy, vol. 6, no. 1, pp. 35–67, 2013.

Digital Library

[168]

R. Zhang and P. Venkitasubramaniam, “Stealthy control signal attacks in linear quadratic Gaussian control systems: Detectability reward tradeoff,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 7, pp. 1555–1570, Jul. 2017.

Digital Library

[169]

X. Fang, S. Misra, G. Xue, and D. Yang, “Smart grid—The new and improved power grid: A survey,” IEEE Commun. Surveys Tuts., vol. 14, no. 4, pp. 944–980, 4th Quart., 2012.

[170]

H. Liang, A. K. Tamang, W. Zhuang, and X. S. Shen, “Stochastic information management in smart grid,” IEEE Commun. Surveys Tuts., vol. 16, no. 3, pp. 1746–1770, 3rd Quart., 2014.

[171]

A. A. Khan, M. H. Rehmani, and M. Reisslein, “Cognitive radio for smart grids: Survey of architectures, spectrum sensing mechanisms, and networking protocols,” IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 860–898, 1st Quart., 2016.

Digital Library

[172]

J. Lopez, J. E. Rubio, and C. Alcaraz, “A resilient architecture for the smart grid,” IEEE Trans. Ind. Informat., vol. 14, no. 8, pp. 3745–3753, Aug. 2018.

[173]

S. Finster and I. Baumgart, “Privacy-aware smart metering: A survey,” IEEE Commun. Surveys Tuts., vol. 17, no. 2, pp. 1088–1101, 2nd Quart., 2015.

[174]

S. Desai, R. Alhadad, N. Chilamkurti, and A. Mahmood, “A survey of privacy preserving schemes in IoE enabled smart grid advanced metering infrastructure,” Cluster Comput., vol. 22, no. 1, pp. 43–69, 2019.

Digital Library

[175]

S. Welikala, C. Dinesh, M. P. B. Ekanayake, R. I. Godaliyadda, and J. Ekanayake, “Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 448–461, Jan. 2017.

[176]

G. W. Hart, “Nonintrusive appliance load monitoring,” Proc. IEEE, vol. 80, no. 12, pp. 1870–1891, Dec. 1992.

[177]

E. J. Aladesanmi and K. A. Folly, “Overview of non-intrusive load monitoring and identification techniques,” IFAC PapersOnLine, vol. 48, no. 30, pp. 415–420, 2015.

[178]

G. W. Arnold, “Challenges and opportunities in smart grid: A position article,” Proc. IEEE, vol. 99, no. 6, pp. 922–927, Jun. 2011.

[179]

J. S. Vardakas, N. Zorba, and C. V. Verikoukis, “A survey on demand response programs in smart grids: Pricing methods and optimization algorithms,” IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 152–178, 1st Quart., 2015.

Digital Library

[180]

R. Anderson and S. Fuloria, “Who controls the off switch?” in Proc. 1st IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), 2010, pp. 96–101.

[181]

P. Gope and B. Sikdar, “An efficient data aggregation scheme for privacy-friendly dynamic pricing-based billing and demand-response management in smart grids,” IEEE Internet Things J., vol. 5, no. 4, pp. 3126–3135, Aug. 2018.

[182]

Z. Zhang, W. Cao, Z. Qin, L. Zhu, Z. Yu, and K. Ren, “When privacy meets economics: Enabling differentially-private battery-supported meter reporting in smart grid,” in Proc. IEEE 25th Int. Symp. Qual. Service (IWQoS), 2017, pp. 1–9.

[183]

Z. Zhang, Z. Qin, L. Zhu, W. Jiang, C. Xu, and K. Ren, “Toward practical differential privacy in smart grid with capacity-limited rechargeable batteries,” arXiv preprint arXiv:1507.03000, 2015.

[184]

P. Barbosa, A. Brito, H. Almeida, and S. Clauß, “Lightweight privacy for smart metering data by adding noise,” in Proc. 29th Annu. ACM Symp. Appl. Comput., 2014, pp. 531–538.

[185]

U. B. Baloglu and Y. Demir, “Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection,” Int. J. Crit. Infrastruct. Protect., vol. 22, pp. 16–24, Sep. 2018.

[186]

X. Liao, P. Srinivasan, D. Formby, and A. R. Beyah, “Di-PriDA: Differentially private distributed load balancing control for the smart grid,” IEEE Trans. Depend. Secure Comput., to be published.

[187]

R. Pal, P. Hui, and V. K. Prasanna, “On optimal privacy engineering for the smart micro-grid,” IEEE Trans. Knowl. Data Eng., to be published.

[188]

J. Xionget al., “Enhancing privacy and availability for data clustering in intelligent electrical service of IoT,” IEEE Internet Things J., vol. 6, no. 2, pp. 1530–1540, Apr. 2019.

[189]

Y. Chen, A. Machanavajjhala, M. Hay, and G. Miklau, “PeGaSus: Data-adaptive differentially private stream processing,” in Proc. ACM SIGSAC Conf. Comput. Commun. Security, 2017, pp. 1375–1388.

[190]

J. Liu, C. Zhang, and Y. Fang, “EPIC: A differential privacy framework to defend smart homes against Internet traffic analysis,” IEEE Internet Things J., vol. 5, no. 2, pp. 1206–1217, Apr. 2018.

[191]

M. Sookhak, H. Tang, Y. He, and F. R. Yu, “Security and privacy of smart cities: A survey, research issues and challenges,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1718–1743, 2nd Quart., 2019.

[192]

J. M. Barrionuevo, P. Berrone, and J. E. Ricart, “Smart cities, sustainable progress,” IESE Insight, vol. 14, no. 14, pp. 50–57, 2012.

[193]

J. Xieet al., “A survey of blockchain technology applied to smart cities: Research issues and challenges,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2794–2830, 3rd Quart., 2019.

[194]

“Smart buildings enable smart cities [Online],” 2016.

[195]

A. Ståhlbröst, A. Sällström, and D. Hollosi, “Audio monitoring in smart cities: An information privacy perspective,” 2014.

[196]

A. W. Burange and H. D. Misalkar, “Review of Internet of Things in development of smart cities with data management & privacy,” in Proc. IEEE Int. Conf. Adv. Comput. Eng. Appl., 2015, pp. 189–195.

[197]

A. Bartoli, J. Hernández-Serrano, M. Soriano, M. Dohler, A. Kountouris, and D. Barthel, “Security and privacy in your smart city,” in Proc. Barcelona Smart Cities Congr., vol. 292, 2011, pp. 1–6.

[198]

P. Pappachanet al., “Towards privacy-aware smart buildings: Capturing, communicating, and enforcing privacy policies and preferences,” in Proc. IEEE 37th Int. Conf. Distrib. Comput. Syst. Workshops (ICDCSW), 2017, pp. 193–198.

[199]

D. Eckhoff and I. Wagner, “Privacy in the smart city—Applications, technologies, challenges, and solutions,” IEEE Commun. Surveys Tuts., vol. 20, no. 1, pp. 489–516, 1st Quart., 2018.

[200]

R. Jia, R. Dong, S. S. Sastry, and C. J. Sapnos, “Privacy-enhanced architecture for occupancy-based HVAC control,” in Proc. ACM/IEEE 8th Int. Conf. Cyber Phys. Syst. (ICCPS), 2017, pp. 177–186.

[201]

S. Ghayyuret al., “IoT-detective: Analyzing IoT data under differential privacy,” in Proc. Int. Conf. Manag. Data, 2018, pp. 1725–1728.

[202]

X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin, “Smart community: An Internet of Things application,” IEEE Commun. Mag., vol. 49, no. 11, pp. 68–75, Nov. 2011.

[203]

C. Laughmanet al., “Power signature analysis,” IEEE Power Energy Mag., vol. 1, no. 2, pp. 56–63, Mar./Apr. 2003.

[204]

A. Lee, “Guidelines for smart grid cyber security,” NIST, Gaithersburg, MD, USA, Rep. 7628, 2010.

[205]

H. Kreutzmann, S. Vollmer, N. Tekampe, and A. Abromeit, “Protection profile for the gateway of a smart metering system,” German Federal Office for Information Security, 2011.

[206]

M. Jawurek, F. Kerschbaum, and G. Danezis, SoK: Privacy Technologies for Smart Grids—A Survey of Options, Microsoft Res., Cambridge, U.K., 2012.

[207]

Z. Erkin, J. R. Troncoso-Pastoriza, R. L. Lagendijk, and F. Pérez-González, “Privacy-preserving data aggregation in smart metering systems: An overview,” IEEE Signal Process. Mag., vol. 30, no. 2, pp. 75–86, Mar. 2013.

[208]

G. Danezis, C. Fournet, M. Kohlweiss, and S. Zanella-Béguelin, “Smart meter aggregation via secret-sharing,” in Proc. 1st ACM Workshop Smart Energy Grid Security, 2013, pp. 75–80.

[209]

C. Rottondi, G. Verticale, and A. Capone, “Privacy-preserving smart metering with multiple data consumers,” Comput. Netw., vol. 57, no. 7, pp. 1699–1713, 2013.

Digital Library

[210]

C. Efthymiou and G. Kalogridis, “Smart grid privacy via anonymization of smart metering data,” in Proc. 1st IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), 2010, pp. 238–243.

[211]

T. Baumeister, Literature Review on Smart Grid Cyber Security, Collaborative Softw. Develop. Lab., Univ. Hawaii, Honolulu, HI, USA, 2010.

[212]

L. Fan and L. Xiong, “Real-time aggregate monitoring with differential privacy,” in Proc. 21st ACM Int. Conf. Inf. Knowl. Manag., 2012, pp. 2169–2173.

[213]

C. Clifton and T. Tassa, “On syntactic anonymity and differential privacy,” in Proc. IEEE 29th Int. Conf. Data Eng. Workshops (ICDEW), 2013, pp. 88–93.

[214]

M. Mukherjee, L. Shu, and D. Wang, “Survey of fog computing: Fundamental, network applications, and research challenges,” IEEE Commun. Surveys Tuts., vol. 20, no. 3, pp. 1826–1857, 3rd Quart., 2018.

[215]

J. Ni, K. Zhang, X. Lin, and X. S. Shen, “Securing fog computing for Internet of Things applications: Challenges and solutions,” IEEE Commun. Surveys Tuts., vol. 20, no. 1, pp. 601–628, 1st Quart., 2018.

[216]

K. D. Anderson, M. E. Bergés, A. Ocneanu, D. Benitez, and J. M. F. Moura, “Event detection for non intrusive load monitoring,” in Proc. IEEE 38th Annu. Conf. Ind. Electron. Soc. (IECON), 2012, pp. 3312–3317.

[217]

Z. Zhou, Y. Qiao, L. Zhu, J. Guan, Y. Liu, and C. Xu, “Differential privacy-guaranteed trajectory community identification over vehicle ad-hoc networks,” Internet Technol. Lett., vol. 1, no. 3, 2018.

[218]

Z. Ma, T. Zhang, X. Liu, X. Li, and K. Ren, “Real-time privacy-preserving data release over vehicle trajectory,” IEEE Trans. Veh. Technol., vol. 86, no. 8, pp. 8091–8102, Aug. 2019.

[219]

B. Nelson and T. Olovsson, “Introducing differential privacy to the automotive domain: Opportunities and challenges,” in Proc. IEEE 86th Veh. Technol. Conf. (VTC-Fall), 2017, pp. 1–7.

[220]

S.-H. An, B.-H. Lee, and D.-R. Shin, “A survey of intelligent transportation systems,” in Proc. IEEE 3rd Int. Conf. Comput. Intell. Commun. Syst. Netw., 2011, pp. 332–337.

[221]

N.-E. El Faouzi, H. Leung, and A. Kurian, “Data fusion in intelligent transportation systems: Progress and challenges—A survey,” Inf. Fusion, vol. 12, no. 1, pp. 4–10, 2011.

Digital Library

[222]

J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen, “Data-driven intelligent transportation systems: A survey,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624–1639, Dec. 2011.

Digital Library

[223]

L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, “Big data analytics in intelligent transportation systems: A survey,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 1, pp. 383–398, Jan. 2019.

[224]

L. Qi, “Research on intelligent transportation system technologies and applications,” in Proc. IEEE Workshop Power Electron. Intell. Transport. Syst. (PEITS), 2008, pp. 529–531.

[225]

D. Eckhoff, R. German, C. Sommer, F. Dressler, and T. Gansen, “SlotSwap: Strong and affordable location privacy in intelligent transportation systems,” IEEE Commun. Mag., vol. 49, no. 11, pp. 126–133, Nov. 2011.

[226]

K. Zheng, Q. Zheng, P. Chatzimisios, W. Xiang, and Y. Zhou, “Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2377–2396, 4th Quart., 2015.

Digital Library

[227]

A. Nshimiyimana, D. Agrawal, and W. Arif, “Comprehensive survey of V2V communication for 4G mobile and wireless technology,” in Proc. IEEE Int. Conf. Wireless Commun. Signal Process. Netw. (WiSPNET), 2016, pp. 1722–1726.

[228]

Q. Shi and M. Abdel-Aty, “Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways,” Transport. Res. C Emerg. Technol., vol. 58, pp. 380–394, Sep. 2015.

[229]

N. Mohamed and J. Al-Jaroodi, “Real-time big data analytics: Applications and challenges,” in Proc. IEEE Int. Conf. High Perform. Comput. Simulat. (HPCS), 2014, pp. 305–310.

[230]

C. Gosman, C. Dobre, and F. Pop, “Privacy-preserving data aggregation in intelligent transportation systems,” in Proc. IFIP/IEEE Symp. Integr. Netw. Service Manag. (IM), 2017, pp. 1059–1064.

[231]

A.-S. K. Pathan, Security of Self-Organizing Networks: MANET, WSN, WMN, VANET. Boca Raton, FL, USA: CRC Press, 2016.

[232]

A. Thaduri, D. Galar, and U. Kumar, “Railway assets: A potential domain for big data analytics,” Procedia Comput. Sci., vol. 53, pp. 457–467, 2015.

[233]

F. Ghofrani, Q. He, R. M. P. Goverde, and X. Liu, “Recent applications of big data analytics in railway transportation systems: A survey,” Transp. Res. C Emerg. Technol., vol. 90, pp. 226–246, May 2018.

[234]

J. Guo, B. Song, Y. He, F. R. Yu, and M. Sookhak, “A survey on compressed sensing in vehicular infotainment systems,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2662–2680, 4th Quart., 2017.

[235]

A. Boualouache, S.-M. Senouci, and S. Moussaoui, “A survey on pseudonym changing strategies for vehicular ad-hoc networks,” IEEE Commun. Surveys Tuts., vol. 20, no. 1, pp. 770–790, 1st Quart., 2018.

[236]

K. Emara, W. Woerndl, and J. H. Schlichter, “Vehicle tracking using vehicular network beacons,” in Proc. IEEE 14th Int. Symp. Workshops World Wireless Mobile Multimedia Netw. (WoWMoM), 2013, pp. 1–6.

[237]

D. Eckhoff, “Privacy and surveillance: Concerns about a future transportation system,” in Proc. 1st GI/ITG KuVS Fachgespräch Inter Veh. Commun. (FG-IVC), 2013, p. 15.

[238]

U. K. Madawala and D. J. Thrimawithana, “A bidirectional inductive power interface for electric vehicles in V2G systems,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4789–4796, Oct. 2011.

[239]

D. T. Hoang, P. Wang, D. Niyato, and E. Hossain, “Charging and discharging of plug-in electric vehicles (PEVS) in vehicle-to-grid (V2G) systems: A cyber insurance-based model,” IEEE Access, vol. 5, pp. 732–754, 2017.

[240]

W. Wei, F. Liu, and S. Mei, “Charging strategies of EV aggregator under renewable generation and congestion: A normalized Nash equilibrium approach,” IEEE Trans. Smart Grid, vol. 7, no. 3, pp. 1630–1641, May 2016.

[241]

M. Zeng, S. Leng, S. Maharjan, S. Gjessing, and J. He, “An incentivized auction-based group-selling approach for demand response management in V2G systems,” IEEE Trans. Ind. Informat., vol. 11, no. 6, pp. 1554–1563, Dec. 2015.

[242]

M. Naor, B. Pinkas, and R. Sumner, “Privacy preserving auctions and mechanism design,” in Proc. 1st ACM Conf. Electron. Commerce, 1999, pp. 129–139.

[243]

M. Pan, J. Sun, and Y. Fang, “Purging the back-room dealing: Secure spectrum auction leveraging Paillier cryptosystem,” IEEE J. Sel. Areas Commun., vol. 29, no. 4, pp. 866–876, Apr. 2011.

Digital Library

[244]

Q. Xiang, L. Kong, X. Liu, J. Xu, and W. Wang, “Auc2Reserve: A differentially private auction for electric vehicle fast charging reservation,” in Proc. IEEE 22nd Int. Conf. Embedded Real Time Comput. Syst. Appl. (RTCSA), 2016, pp. 85–94.

[245]

K. Al-Hussaeni, B. C. M. Fung, F. Iqbal, G. G. Dagher, and E. G. Park, “SafePath: Differentially-private publishing of passenger trajectories in transportation systems,” Comput. Netw., vol. 143, pp. 126–139, Oct. 2018.

Digital Library

[246]

X. Ma, J. Ma, H. Li, Q. Jiang, and S. Gao, “AGENT: An adaptive geo-indistinguishable mechanism for continuous location-based service,” Peer-to-Peer Netw. Appl., vol. 11, no. 3, pp. 473–485, 2018.

[247]

C. Xu, L. Zhu, Y. Liu, J. Guan, and S. Yu, “DP-LTOD: Differential privacy latent trajectory community discovering services over location-based social networks,” IEEE Trans. Services Comput., to be published.

[248]

A. Machanavajjhala and X. He, “Analyzing your location data with provable privacy guarantees,” in Handbook of Mobile Data Privacy. Cham, Switzerland: Springer, 2018, pp. 97–127.

[249]

W. Zhang, R. Rao, G. Cao, and G. Kesidis, “Secure routing in ad hoc networks and a related intrusion detection problem,” in Proc. MILCOM, vol. 2, 2003, pp. 735–740.

[250]

T. Anantvalee and J. Wu, “A survey on intrusion detection in mobile ad hoc networks,” in Wireless Network Security. Boston, MA, USA: Springer, 2007, pp. 159–180.

[251]

A. Narayanan and V. Shmatikov, “Myths and fallacies of ‘personally identifiable information,”’ Commun. ACM, vol. 53, no. 6, pp. 24–26, 2010.

Digital Library

[252]

X. Gao, B. Firner, S. Sugrim, V. Kaiser-Pendergrast, Y. Yang, and J. Lindqvist, “Elastic pathing: Your speed is enough to track you,” in Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Comput. (UbiComp), 2014, pp. 975–986.

[253]

A. Tockar, “Riding with the stars: Passenger privacy in the NYC taxicab dataset,” Neustar Res., Sterling, VA, USA, 2014.

[254]

Q. Wang, X. Liu, J. Du, and F. Kong, “Smart charging for electric vehicles: A survey from the algorithmic perspective,” IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1500–1517, 2nd Quart., 2016.

Digital Library

[255]

Y. Zhang, J. Li, D. Zheng, P. Li, and Y. Tian, “Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice,” J. Netw. Comput. Appl., vol. 122, pp. 50–60, Nov. 2018.

[256]

Z. Pang, “Technologies and architectures of the Internet-of-Things (IoT) for health and well-being,” Ph.D. dissertation, Electron. Comput. Syst., KTH Roy. Inst. Technol., Stockholm, Sweden, 2013.

[257]

S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, “The Internet of Things for health care: A comprehensive survey,” IEEE Access, vol. 3, pp. 678–708, 2015.

[258]

A. Rizwanet al., “A review on the role of nano-communication in future healthcare systems: A big data analytics perspective,” IEEE Access, vol. 6, pp. 41903–41920, 2018.

[259]

Y. Yang, X. Zheng, W. Guo, X. Liu, and V. Chang, “Privacy-preserving fusion of IoT and big data for e-Health,” Future Gener. Comput. Syst., vol. 86, pp. 1437–1455, Sep. 2018.

[260]

N. Sharma and R. Bhatt, “Privacy preservation in WSN for healthcare application,” Procedia Comput. Sci., vol. 132, pp. 1243–1252, 2018.

Digital Library

[261]

M. M. Alam, H. Malik, M. I. Khan, T. Pardy, A. Kuusik, and Y. L. Moullec, “A survey on the roles of communication technologies in IoT-based personalized healthcare applications,” IEEE Access, vol. 6, pp. 36611–36631, 2018.

[262]

Z. Guan, Z. Lv, X. Du, L. Wu, and M. Guizani, “Achieving data utility-privacy tradeoff in Internet of medical things: A machine learning approach,” Future Gener. Comput. Syst., vol. 98, pp. 60–68, Sep. 2019.

[263]

B. K. Beaulieu-Jones, W. Yuan, S. G. Finlayson, and Z. S. Wu, “Privacy-preserving distributed deep learning for clinical data,” arXiv preprint arXiv:1812.01484, 2018.

[264]

A. Alnemari, C. J. Romanowski, and R. K. Raj, “An adaptive differential privacy algorithm for range queries over healthcare data,” in Proc. IEEE Int. Conf. Healthcare Informat. (ICHI), 2017, pp. 397–402.

[265]

J. L. Raisaroet al., “Protecting privacy and security of genomic data in i2b2 with homomorphic encryption and differential privacy,” IEEE/ACM Trans. Comput. Biol. Bioinformat., vol. 15, no. 5, pp. 1413–1426, Sep. 2018.

Digital Library

[266]

W. Tang, J. Ren, K. Deng, and Y. Zhang, “Secure data aggregation of lightweight e-Healthcare IoT devices with fair incentives,” IEEE Internet Things J., to be published.

[267]

A. C. Valdez and M. Ziefle, “The users’ perspective on the privacy-utility trade-offs in health recommender systems,” Int. J. Human–Comput. Studies, vol. 121, pp. 108–121, Jan. 2019.

[268]

J. Lane and C. Schur, “Balancing access to health data and privacy: A review of the issues and approaches for the future,” Health Services Res., vol. 45, pp. 1456–1467, Aug. 2010.

[269]

D. Hemapriya, P. Viswanath, V. Mithra, S. Nagalakshmi, and G. Umarani, “Wearable medical devices—Design challenges and issues,” in Proc. IEEE Int. Conf. Innov. Green Energy Healthcare Technol. (IGEHT), 2017, pp. 1–6.

[270]

W. J. Long and W. Lin, “An authentication protocol for wearable medical devices,” in Proc. 13th IEEE Int. Conf. Expo Emerg. Technol. Smarter World (CEWIT), 2017, pp. 1–5.

[271]

A. Cavoukian, A. Fisher, S. Killen, and D. A. Hoffman, “Remote home health care technologies: How to ensure privacy? Build it in: Privacy by design,” Identity Inf. Soc., vol. 3, no. 2, pp. 363–378, 2010.

[272]

B. Shickel, P. Tighe, A. Bihorac, and P. Rashidi, “Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis,” arXiv preprint arXiv:1706.03446, 2017.

[273]

T. Sahama, L. Simpson, and B. Lane, “Security and privacy in eHealth: Is it possible?” in Proc. IEEE 15th Int. Conf. e-Health Netw. Appl. Services (Healthcom), 2013, pp. 249–253.

[274]

R. Agrawal and R. Srikant, “Privacy-preserving data mining,” ACM SIGMOD Rec., vol. 29, no. 2, pp. 439–450, 2000.

Digital Library

[275]

X. Jiang, S. Cheng, and L. Ohno-Machado, “Quantifying fine-grained privacy risk and representativeness in medical data,” in Proc. ACM Workshop Data Min. Med. Healthcare, 2011, pp. 64–67.

[276]

P. Shi, L. Xiong, and B. C. M. Fung, “Anonymizing data with quasi-sensitive attribute values,” in Proc. 19th ACM Int. Conf. Inf. Knowl. Manag., 2010, pp. 1389–1392.

[277]

S. Sharma, K. Chen, and A. P. Sheth, “Toward practical privacy-preserving analytics for IoT and cloud-based healthcare systems,” IEEE Internet Comput., vol. 22, no. 2, pp. 42–51, May 2018.

[278]

W. Zhang, H. Zou, L. Luo, Q. Liu, W. Wu, and W. Xiao, “Predicting potential side effects of drugs by recommender methods and ensemble learning,” Neurocomputing, vol. 173, pp. 979–987, Jan. 2016.

Digital Library

[279]

Q. Zhang, G. Zhang, J. Lu, and D. Wu, “A framework of hybrid recommender system for personalized clinical prescription,” in Proc. IEEE 10th Int. Conf. Intell. Syst. Knowl. Eng. (ISKE), 2015, pp. 189–195.

[280]

B. Esteban, Á. Tejeda-Lorente, C. Porcel, M. Arroyo, and E. Herrera-Viedma, “TPLUFIB-WEB: A fuzzy linguistic Web system to help in the treatment of low back pain problems,” Knowl. Based Syst., vol. 67, pp. 429–438, Sep. 2014.

Digital Library

[281]

R. J. Bayardo and R. Agrawal, “Data privacy through optimal k-anonymization,” in Proc. IEEE 21st Int. Conf. Data Eng. (ICDE), 2005, pp. 217–228.

[282]

B. Yüksel, A. Küpçü, and Ö. Özkasap, “Research issues for privacy and security of electronic health services,” Future Gener. Comput. Syst., vol. 68, pp. 1–13, Mar. 2017.

[283]

G. Fimiani, “Supporting privacy in a cloud-based health information system by means of fuzzy conditional identity-based proxy re-encryption (FCI-PRE),” in Proc. IEEE 32nd Int. Conf. Adv. Inf. Netw. Appl. Workshops (WAINA), May 2018, pp. 569–572.

[284]

C. Yin, J. Xi, R. Sun, and J. Wang, “Location privacy protection based on differential privacy strategy for big data in industrial Internet-of-Things,” IEEE Trans. Ind. Informat., vol. 14, no. 8, pp. 3628–3636, Aug. 2017.

[285]

J. Giraldo, A. Cardenas, and M. Kantarcioglu, “Security and privacy trade-offs in CPS by leveraging inherent differential privacy,” in Proc. IEEE Conf. Control Technol. Appl. (CCTA), 2017, pp. 1313–1318.

[286]

P. Li, T. Li, H. Ye, J. Li, X. Chen, and Y. Xiang, “Privacy-preserving machine learning with multiple data providers,” Future Gener. Comput. Syst., vol. 87, pp. 341–350, Oct. 2018.

Digital Library

[287]

K. Ashton. (Jun. 2009). Internet of Things RFID Journal. [Online]. Available: http://www.rfidjournal.com/articles/view?4986

[288]

F. Javed, M. K. Afzal, M. Sharif, and B.-S. Kim, “Internet of Things (IoT) operating systems support, networking technologies, applications, and challenges: A comparative review,” IEEE Commun. Surveys Tuts., vol. 20, no. 3, pp. 2062–2100, 3rd Quart., 2018.

[289]

Y. Li, M. Hou, H. Liu, and Y. Liu, “Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things,” Inf. Technol. Manag., vol. 13, no. 4, pp. 205–216, 2012.

[290]

K. R. Sollins, “IoT big data security and privacy vs. innovation,” IEEE Internet Things J., vol. 6, no. 2, pp. 1628–1635, Apr. 2019.

[291]

L. M. Thompson, Industrial Data Communications. Research Triangle Park, NC, USA: ISA, 2007.

[292]

H. Li, A. D. Dimitrovski, J. B. Song, Z. Han, and L. Qian, “Communication infrastructure design in cyber physical systems with applications in smart grids: A hybrid system framework,” IEEE Commun. Surveys Tuts., vol. 16, no. 3, pp. 1689–1708, 3rd Quart., 2014.

[293]

X. Lu, Q. Li, Z. Qu, and P. Hui, “Privacy information security classification study in Internet of Things,” in Proc. IEEE Int. Conf. Identification Inf. Knowl. Internet Things (IIKI), 2014, pp. 162–165.

[294]

C. M. Medaglia and A. Serbanati, “An overview of privacy and security issues in the Internet of Things,” in The Internet of Things. New York, NY, USA: Springer, 2010, pp. 389–395.

[295]

P. De Leusse, P. Periorellis, T. Dimitrakos, and S. K. Nair, “Self managed security cell, a security model for the Internet of Things and services,” in Proc. 1st Int. Conf. Adv. Future Internet, 2009, pp. 47–52.

[296]

B. Shen and Y. Liu, “Privacy and security in the exploitation of Internet of Things,” J. Dialectics Nat., vol. 33, no. 6, pp. 77–83, 2011.

[297]

Y. Sun, J. Zhang, Y. Xiong, and G. Zhu, “Data security and privacy in cloud computing,” Int. J. Distrib. Sensor Netw., vol. 10, no. 7, 2014, Art. no.

[298]

L. Sweeney, “Achieving k-anonymity privacy protection using generalization and suppression,” Int. J. Uncertainty Fuzziness Knowl. Syst., vol. 10, no. 5, pp. 571–588, 2002.

Digital Library

[299]

Y. Saygin, V. S. Verykios, and A. K. Elmagarmid, “Privacy preserving association rule mining,” in Proc. IEEE 12th Int. Workshop Res. Issues Data Eng. Eng. E-Commerce E-Bus. Syst. (RIDE-2EC), 2002, pp. 151–158.

[300]

A. C.-C. Yao, “How to generate and exchange secrets,” in Proc. IEEE 27th Annu. Symp. Found. Comput. Sci., 1986, pp. 162–167.

[301]

C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, “Tools for privacy preserving distributed data mining,” ACM SIGKDD Explor. Newslett., vol. 4, no. 2, pp. 28–34, 2002.

Digital Library

[302]

V. Tudor, V. Gulisano, M. Almgren, and M. Papatriantafilou, “BES: Differentially private event aggregation for large-scale IoT-based systems,” Future Gener. Comput. Syst., to be published.

[303]

L. Bassi, “Industry 4.0: Hope, hype or revolution?” in Proc. IEEE 3rd Int. Forum Res. Technol. Soc. Ind. (RTSI), 2017, pp. 1–6.

[304]

M. Wollschlaeger, T. Sauter, and J. Jasperneite, “The future of industrial communication: Automation networks in the era of the Internet of Things and industry 4.0,” Ind. Electron. Mag., vol. 11, no. 1, pp. 17–27, Mar. 2017.

[305]

Z. Lv, H. Song, P. Basanta-Val, A. Steed, and M. Jo, “Next-generation big data analytics: State of the art, challenges, and future research topics,” IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 1891–1899, Aug. 2017.

[306]

F. Xiao, L.-T. Sha, Z.-P. Yuan, and R.-C. Wang, “VulHunter: A discovery for unknown bugs based on analysis for known patches in industry Internet of Things,” IEEE Trans. Emerg. Topics Comput., to be published.

[307]

C. Yin, S. Zhang, J. Xi, and J. Wang, “An improved anonymity model for big data security based on clustering algorithm,” Concurrency Comput. Pract. Exp., vol. 29, no. 7, 2017, Art. no.

[308]

R. W. Brennan, “Toward real-time distributed intelligent control: A survey of research themes and applications,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 5, pp. 744–765, Sep. 2007.

Digital Library

[309]

H. Parunak, “Autonomous agent architectures: A non-technical introduction,” Ind. Technol. Inst., Colombo, Sri Lanka, Rep., 1993.

[310]

K. Zetter. (Jan. 2015). A Cyberattack Has Caused Confirmed Physical Damage for the Second Time Ever, Wired. [Online]. Available: http://www.wired.com/2015/01/german-steel-mill-hack-destruction

[311]

K. Chatzikokolakis, M. E. Andrés, N. E. Bordenabe, and C. Palamidessi, “Broadening the scope of differential privacy using metrics,” in Proc. Int. Symp. Privacy Enhanc. Technol. Symp., 2013, pp. 82–102.

[312]

T. Song, R. Li, B. Mei, J. Yu, X. Xing, and X. Cheng, “A privacy preserving communication protocol for IoT applications in smart homes,” in Proc. IEEE Int. Conf. Identification Inf. Knowl. Internet Things (IIKI), 2016, pp. 519–524.

[313]

R. Li, T. Song, N. Capurso, J. Yu, J. Couture, and X. Cheng, “IoT applications on secure smart shopping system,” IEEE Internet Things J., vol. 4, no. 6, pp. 1945–1954, Dec. 2017.

[314]

T. Song, N. Capurso, X. Cheng, J. Yu, B. Chen, and W. Zhao, “Enhancing GPS with lane-level navigation to facilitate highway driving,” IEEE Trans. Veh. Technol., vol. 66, no. 6, pp. 4579–4591, Jun. 2017.

[315]

Y. Liang, Z. Cai, Q. Han, and Y. Li, “Location privacy leakage through sensory data,” Security Commun. Netw., vol. 2017, Aug. 2017, Art. no.

[316]

H.-J. Yim, D. Seo, H. Jung, M.-K. Back, I. Kim, and K.-C. Lee, “Description and classification for facilitating interoperability of heterogeneous data/events/services in the Internet of Things,” Neurocomputing, vol. 256, pp. 13–22, Sep. 2017.

Digital Library

[317]

Z. Cui, Y. Cao, X. Cai, J. Cai, and J. Chen, “Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things,” J. Parallel Distrib. Comput., vol. 132, pp. 217–229, Oct. 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0743731517303453

Digital Library

[318]

Z. Song, Y. Sun, J. Wan, L. Huang, Y. Xu, and C.-H. Hsu, “Exploring robustness management of social Internet of Things for customization manufacturing,” Future Gener. Comput. Syst., vol. 92, pp. 846–856, Mar. 2019.

[319]

C. Li and G. Miklau, “Optimal error of query sets under the differentially-private matrix mechanism,” in Proc. 16th ACM Int. Conf. Database Theory, 2013, pp. 272–283.

[320]

A.-R. Sadeghi, C. Wachsmann, and M. Waidner, “Security and privacy challenges in industrial Internet of Things,” in Proc. 52nd ACM/EDAC/IEEE Design Autom. Conf. (DAC), 2015, pp. 1–6.

[321]

J. Sathishkumar and D. R. Patel, “Enhanced location privacy algorithm for wireless sensor network in Internet of Things,” in Proc. IEEE Int. Conf. Internet Things Appl. (IOTA), 2016, pp. 208–212.

[322]

D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Trans. Ind. Informat., vol. 12, no. 1, pp. 425–436, Feb. 2016.

[323]

M. R. Asghar, G. Dán, D. Miorandi, and I. Chlamtáč, “Smart meter data privacy: A survey,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2820–2835, 4th Quart., 2017.

[324]

D. Li, Q. Yang, W. Yu, D. An, X. Yang, and W. Zhao, “A strategy-proof privacy-preserving double auction mechanism for electrical vehicles demand response in microgrids,” in Proc. IEEE 36th Int. Perform. Comput. Commun. Conf. (IPCCC), 2017, pp. 1–8.

[325]

A. V. D. M. Kayem, C. Meinel, and S. D. Wolthusen, “A smart micro-grid architecture for resource constrained environments,” in Proc. IEEE 31st Int. Conf. Adv. Inf. Netw. Appl. (AINA), 2017, pp. 857–864.

[326]

P. L. Ambassa, A. V. D. M. Kayem, S. D. Wolthusen, and C. Meinel, “Privacy risks in resource constrained smart micro-grids,” in Proc. IEEE 32nd Int. Conf. Adv. Inf. Netw. Appl. Workshops (WAINA), May 2018, pp. 527–532.

[327]

M. Gohar, M. Muzammal, and A. U. Rahman, “SMART TSS: Defining transportation system behavior using big data analytics in smart cities,” Sustain. Cities Soc., vol. 41, pp. 114–119, Aug. 2018.

[328]

M.-C. Chuang and J.-F. Lee, “PPAS: A privacy preservation authentication scheme for vehicle-to-infrastructure communication networks,” in Proc. IEEE Int. Conf. Consum. Electron. Commun. Netw. (CECNet), 2011, pp. 1509–1512.

[329]

S. Purri, T. Choudhury, N. Kashyap, and P. Kumar, “Specialization of IoT applications in health care industries,” in Proc. IEEE Int. Conf. Big Data Anal. Comput. Intell. (ICBDAC), 2017, pp. 252–256.

[330]

L. Patrono, P. Primiceri, P. Rametta, I. Sergi, and P. Visconti, “An innovative approach for monitoring elderly behavior by detecting home appliance’s usage,” in Proc. 25th IEEE Int. Conf. Softw. Telecommun. Comput. Netw. (SoftCOM), 2017, pp. 1–7.

[331]

V. S. Alagar, K. Periyasamy, and K. Wan, “Privacy and security for patient-centric elderly health care,” in Proc. IEEE 19th Int. Conf. e-Health Netw. Appl. Services (Healthcom), 2017, pp. 1–6.

[332]

Policy Engagement Network, “Electronic health privacy and security in developing countries and humanitarian operations,” Protecting Med. Inf. eHealth Projects, London School Econ. Political Sci., London, U.K., Rep., pp. 1–28, 2010.

[333]

W. Gao, W. Yu, F. Liang, W. G. Hatcher, and C. Lu, “Privacy-preserving auction for big data trading using homomorphic encryption,” IEEE Trans. Netw. Sci. Eng., to be published.

[334]

J. Murtagh and S. Vadhan, “The complexity of computing the optimal composition of differential privacy,” in Proc. Theory Cryptography Conf., 2016, pp. 157–175.

[335]

J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao, “PrivBayes: Private data release via Bayesian networks,” ACM Trans. Database Syst., vol. 42, no. 4, pp. 1–41, Oct. 2017. [Online]. Available: http://doi.acm.org/10.1145/3134428

Digital Library

[336]

R. Chen, Q. Xiao, Y. Zhang, and J. Xu, “Differentially private high-dimensional data publication via sampling-based inference,” in Proc. 21st ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., 2015, pp. 129–138.

[337]

P. Kairouz, K. Bonawitz, and D. Ramage, “Discrete distribution estimation under local privacy,” arXiv preprint arXiv:1602.07387, 2016.

[338]

C. Dwork and G. N. Rothblum, “Concentrated differential privacy,” arXiv preprint arXiv:1603.01887, 2016.

[339]

G. Kellaris, S. Papadopoulos, X. Xiao, and D. Papadias, “Differentially private event sequences over infinite streams,” Proc. VLDB Endow., vol. 7, no. 12, pp. 1155–1166, 2014.

Digital Library

[340]

C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum, “Differential privacy under continual observation,” in Proc. 42nd ACM Symp. Theory Comput., 2010, pp. 715–724.

[341]

Z. Cui, F. Xue, X. Cai, Y. Cao, G.-G. Wang, and J. Chen, “Detection of malicious code variants based on deep learning,” IEEE Trans. Ind. Informat., vol. 14, no. 7, pp. 3187–3196, Jul. 2018.

[342]

C. Elkan, “Preserving privacy in data mining via importance weighting,” in Proc. Int. Workshop Privacy Security Issues Data Min. Mach. Learn., 2010, pp. 15–21.

[343]

M. Senekane, M. Mafu, and B. M. Taele, “Privacy-preserving quantum machine learning using differential privacy,” in Proc. IEEE AFRICON, 2017, pp. 1432–1435.

[344]

X. Ma, J. Ma, S. Gao, and Q. Yao, “APDL: A practical privacy-preserving deep learning model for smart devices,” in Proc. Int. Conf. Mobile Ad Hoc Sensor Netw., 2017, pp. 377–390.

[345]

R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Gener. Comput. Syst., vol. 25, no. 6, pp. 599–616, 2009.

Digital Library

[346]

Y. Li, H. Yu, B. Song, and J. Chen, “Image encryption based on a single-round dictionary and chaotic sequences in cloud computing,” Concurrency Comput. Pract. Exp., Mar. 2019, Art. no.

[347]

Z. Sanaei, S. Abolfazli, A. Gani, and R. Buyya, “Heterogeneity in mobile cloud computing: Taxonomy and open challenges,” IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 369–392, 4th Quart., 2014.

[348]

Z. Xiao and Y. Xiao, “Security and privacy in cloud computing,” IEEE Commun. Surveys Tuts., vol. 15, no. 2, pp. 843–859, 2nd Quart., 2013.

[349]

X. Zhanget al., “MRMondrian: Scalable multidimensional anonymisation for big data privacy preservation,” IEEE Trans. Big Data to be published.

[350]

M. Zhang, H. Wang, Z. Cui, and J. Chen, “Hybrid multi-objective cuckoo search with dynamical local search,” Memetic Comput., vol. 10, no. 2, pp. 199–208, 2018.

[351]

S. Sharma, J. Powers, and K. Chen, “PrivateGraph: Privacy-preserving spectral analysis of encrypted graphs in the cloud,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 5, pp. 981–995, May 2018.

[352]

M. T. Hale and M. Egerstedt, “Cloud-enabled differentially private multi-agent optimization with constraints,” IEEE Trans. Control Netw. Syst., vol. 5, no. 4, pp. 1693–1706, Dec. 2018.

[353]

M. Yang, A. Margheri, R. Hu, and V. Sassone, “Differentially private data sharing in a cloud federation with blockchain,” IEEE Cloud Comput., vol. 5, no. 6, pp. 69–79, Nov./Dec. 2018.

[354]

L. Kong, D. Zhang, Z. He, Q. Xiang, J. Wan, and M. Tao, “Embracing big data with compressive sensing: A green approach in industrial wireless networks,” IEEE Commun. Mag., vol. 54, no. 10, pp. 53–59, Oct. 2016.

Digital Library

[355]

M. Du, K. Wang, Z. Xia, and Y. Zhang, “Differential privacy preserving of training model in wireless big data with edge computing,” IEEE Trans. Big Data. to be published.

[356]

M. Du, K. Wang, X. Liu, S. Guo, and Y. Zhang, “A differential privacy-based query model for sustainable fog data centers,” IEEE Trans. Sustain. Comput., vol. 4, no. 2, pp. 145–155, Apr.–Jun. 2019.

[357]

G. Karame and S. Capkun, “Blockchain security and privacy,” IEEE Security Privacy, vol. 16, no. 4, pp. 11–12, Jul./Aug. 2018.

[358]

F. Gao, L. Zhu, M. Shen, K. Sharif, Z. Wan, and K. Ren, “A blockchain-based privacy-preserving payment mechanism for vehicle-to-grid networks,” IEEE Netw., vol. 32, no. 6, pp. 184–192, Nov./Dec. 2018.

[359]

Z. Xiong, Y. Zhang, D. Niyato, P. Wang, and Z. Han, “When mobile blockchain meets edge computing: Challenges and applications,” arXiv preprint arXiv:1711.05938, 2017.

[360]

M. Banerjee, J. Lee, and K.-K. R. Choo, “A blockchain future for Internet of Things security: A position paper,” Digit. Commun. Netw., vol. 4, no. 3, pp. 149–160, 2018.

[361]

A. Alnemariet al., “Protecting infrastructure data via enhanced access control, blockchain and differential privacy,” in Proc. Int. Conf. Crit. Infrastruct. Protect., 2018, pp. 113–125.

[362]

R. Henry, A. Herzberg, and A. Kate, “Blockchain access privacy: Challenges and directions,” IEEE Security Privacy, vol. 16, no. 4, pp. 38–45, Jul./Aug. 2018.

Digital Library

[363]

G. Zyskind, O. Nathan, and A. Pentland, “Decentralizing privacy: Using blockchain to protect personal data,” in Proc. IEEE Security Privacy Workshops (SPW), 2015, pp. 180–184.

[364]

M. U. Hassan, M. H. Rehmani, and J. Chen, “Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions,” Future Gener. Comput. Syst., vol. 97, pp. 512–529, Aug. 2019.

Digital Library

[365]

H. Halpin and M. Piekarska, “Introduction to security and privacy on the blockchain,” in Proc. IEEE Eur. Symp. Security Privacy Workshops (EuroS&PW), 2017, pp. 1–3.

[366]

X. Liang and Y. Xiao, “Game theory for network security,” IEEE Commun. Surveys Tuts., vol. 15, no. 1, pp. 472–486, 1st Quart., 2013.

[367]

M. Pilz and L. Al-Fagih, “Recent advances in local energy trading in the smart grid based on game–theoretic approaches,” IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 1363–1371, Mar. 2019.

[368]

X. Wu, T. Wu, M. Khan, Q. Ni, and W. Dou, “Game theory based correlated privacy preserving analysis in big data,” IEEE Trans. Big Data, to be published.

[369]

L. Xu, C. Jiang, Y. Qian, J. Li, Y. Zhao, and Y. Ren, “Privacy-accuracy trade-off in differentially-private distributed classification: A game theoretical approach,” IEEE Trans. Big Data, to be published.

Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Zhu LChen X(2025)Privacy protection in federated learning: a study on the combined strategy of local and global differential privacyThe Journal of Supercomputing10.1007/s11227-024-06845-981:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06845-9
Torre DChennamaneni AJo JVyas GSabrsula B(2024)Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/369599834:2(1-48)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3695998
Show More Cited By

Index Terms

Differential Privacy Techniques for Cyber Physical Systems: A Survey
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime
    2. Privacy policies

Index terms have been assigned to the content through auto-classification.

Recommendations

Cyber-Physical Systems: Integrated Computing and Engineering Design
Cyber physical systems security

Cyber Physical Systems (CPS) are networked systems of cyber (computation and communication) and physical (sensors and actuators) components that interact in a feedback loop with the possible help of human intervention, interaction and utilization. These ...
A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems
Abstract
Cyber-Physical Systems (CPSs), which integrate control, computing, and communication, are considered as next-generation intelligent systems.A major concern in CPS is to ensure security. If security is not ensured, then it leads to both property ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 22, Issue 1

Firstquarter 2020

736 pages

ISSN:1553-877X

Issue’s Table of Contents

1553-877X © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Press

Publication History

Published: 01 January 2020

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Zhu LChen X(2025)Privacy protection in federated learning: a study on the combined strategy of local and global differential privacyThe Journal of Supercomputing10.1007/s11227-024-06845-981:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06845-9
Torre DChennamaneni AJo JVyas GSabrsula B(2024)Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/369599834:2(1-48)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3695998
Curran NCho MFeng RLiu LTang BMohajerAnsari PDomeke APesé MShin KFritz MKrauß CHof H(2024)Achieving the Safety and Security of the End-to-End AV PipelineProceedings of the 2024 Cyber Security in CarS Workshop10.1145/3689936.3694694(13-24)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689936.3694694
Pillai SHu WLo DGamess E(2024)Privacy-Preserving Gross Domestic Product (GDP) Calculation Using Paillier Encryption and Differential PrivacyProceedings of the 2024 ACM Southeast Conference10.1145/3603287.3651188(182-187)Online publication date: 18-Apr-2024
https://dl.acm.org/doi/10.1145/3603287.3651188
Gao CLu JLou JLiu YYu W(2024)Privacy-Preserving Algorithm for APPs in Vehicle Intelligent Terminal System: A Compressive MethodIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344516325:11(17352-17365)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3445163
Yuan TChung HFu X(2024)PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in CommunicationsIEEE Network: The Magazine of Global Internetworking10.1109/MNET.2023.333087738:5(196-203)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/MNET.2023.3330877
Shen MWang JDu HNiyato DTang XKang JDing YZhu L(2024)Secure Semantic Communications: Challenges, Approaches, and OpportunitiesIEEE Network: The Magazine of Global Internetworking10.1109/MNET.2023.332711138:4(197-206)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/MNET.2023.3327111
Wang YLin HLam JKwok K(2024)Differentially private consensus and distributed optimization in multi-agent systemsNeurocomputing10.1016/j.neucom.2024.127986597:COnline publication date: 7-Sep-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127986
Pan KOng YGong MLi HQin AGao Y(2024)Differential privacy in deep learningNeurocomputing10.1016/j.neucom.2024.127663589:COnline publication date: 7-Jul-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127663
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents