survey

A Survey on Differential Privacy for Unstructured Data Content

Authors:

Jinjun ChenAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 10s

Article No.: 207, Pages 1 - 28

https://doi.org/10.1145/3490237

Published: 13 September 2022 Publication History

Abstract

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously generated and shared, and it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before it is shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also discuss their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.

References

[1]

Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, and Ziteng Sun. 2020. Context aware local differential privacy. In 37th International Conference on Machine Learning (ICML’20). 52–62.

Digital Library

[2]

Balamurugan Anandan, Chris Clifton, Wei Jiang, Mummoorthy Murugesan, Pedro Pastrana-Camacho, and Luo Si. 2012. t-Plausibility: Generalizing words to desensitize text. Transactions on Data Privacy 5, 3 (2012), 505–534.

Digital Library

[3]

Miguel E. Andrés, Nicolás E. Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013. Geo-indistinguishability: Differential privacy for location-based systems. In Proc. of the 20th ACM SIGSAC Conference on Computer and Communications Security (CCS’13). 901–914.

Digital Library

[4]

Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits. 2017. BLENDER: Enabling local search with a hybrid differential privacy model. In 26th USENIX Security Symposium. 747–764.

[5]

Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim. 2020. Private summation in the multi-message shuffle model. In Proc. of the 27th ACM SIGSAC Conference on Computer and Communications Security (CCS’20). 657–676.

Digital Library

[6]

Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet. 2012. The Johnson-Lindenstrauss transform itself preserves differential privacy. In 53rd IEEE Annual Symposium on Foundations of Computer Science (FOCS’12). 410–419.

[7]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5 (2017), 135–146.

[8]

Efe Bozkir, Onur Gunlu, Wolfgang Fuhl, Rafael F. Schaefer, and Enkelejda Kasneci. 2020. Differential Privacy for eye tracking with temporal correlations. Cryptology ePrint Archive, Report 2020/340. https://eprint.iacr.org/2020/340.

[9]

Xingjuan Cai, Shaojin Geng, Di Wu, Jianghui Cai, and Jinjun Chen. 2021. A multi-cloud model based many-objective intelligent algorithm for efficient task scheduling in Internet of Things. IEEE Internet of Things Journal 8, 12 (2021), 9645–9653. DOI:

[10]

Frank Cangialosi, Neil Agarwal, Venkat Arun, Junchen Jiang, Srinivas Narayana, Anand Sarwate, and Ravi Netravali. 2022. Privid: Practical, privacy-preserving video analytics queries. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI). 209–228.

[11]

Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. 2013. Broadening the scope of differential privacy using metrics. In The 13th Privacy Enhancing Technologies Symposium (PETS’13). 82–102.

[12]

Konstantinos Chatzikokolakis, Catuscia Palamidessi, and Marco Stronati. 2015. Constructing elastic distinguishability metrics for location privacy. Proceedings on Privacy Enhancing Technologies. 2 (2015), 156–170.

[13]

Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu, and Chun-Shien Lu. 2021. Perceptual Indistinguishability-Net (PI-Net): Facial image obfuscation with manipulable semantics. In Proc. of the 34th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21). 6478–6487.

[14]

Raj Chetty and John N. Friedman. 2019. A practical method to reduce privacy loss when disclosing statistics based on small samples. AEA Papers and Proceedings 109 (2019), 414–20.

[15]

Marcus Cuda Christoph Ruegg and Jurgen Van Gael. 2009. Distance metrics. [EB/OL]. https://numerics.mathdotnet.com/Distance.html.

[16]

Graham Cormode, Somesh Jha, Tejas Kulkarni, Ninghui Li, Divesh Srivastava, and Tianhao Wang. 2018. Privacy at scale: Local differential privacy in practice. In Proc. of the 44th International Conference on Management of Data (SIGMOD’18). 1655–1658.

Digital Library

[17]

Zhihua Cui, Fei Xue, Shiqiang Zhang, Xingjuan Cai, Yang Cao, Wensheng Zhang, and Jinjun Chen. 2020. A hybrid blockchain-based identity authentication scheme for multi-WSN. IEEE Transactions on Services Computing 13, 2 (2020), 241–251.

[18]

Fida Kamal Dankar and Khaled El Emam. 2013. Practicing differential privacy in health care: A review. Transactions on Data Privacy 6, 1 (2013), 35–67.

Digital Library

[19]

Damien Desfontaines and Balázs Pejó. 2020. SoK: Differential privacies. Proceedings on Privacy Enhancing Technologies 2020, 2 (2020), 288–313.

[20]

John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. 2013. Local privacy and statistical minimax rates. In 54th IEEE Annual Symposium on Foundations of Computer Science (FOCS’13). 429–438.

[21]

Cynthia Dwork. 2008. Differential privacy: A survey of results. Theory and Applications of Models of Computation 4978 (2008), 1–19.

[22]

Cynthia Dwork. 2009. The differential privacy frontier. In The 6th Theory of Cryptography Conference. 496–502.

Digital Library

[23]

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In The 3th Theory of Cryptography Conference (TCC’06). 265–284.

Digital Library

[24]

Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman. 2017. Exposed! A survey of attacks on private data. Annual Review of Statistics and Its Application 4, 1 (2017), 61–84.

[25]

Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In Proc. of the 21st ACM SIGSAC Conference on Computer and Communications Security (CCS’14). 1054–1067.

Digital Library

[26]

Liyue Fan. 2018. Image pixelization with differential privacy. In 32nd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec’18). 148–162.

Digital Library

[27]

Liyue Fan. 2019. Practical image obfuscation with provable privacy. In 20th IEEE International Conference on Multimedia and Expo (ICME’19). 784–789.

[28]

Liyue Fan. 2020. A survey of differentially private generative adversarial networks. In The AAAI Workshop on Privacy-Preserving Artificial Intelligence.

[29]

Liyue Fan and Luca Bonomi. 2018. Time series sanitization with metric-based privacy. In 6th IEEE International Congress on Big Data. 264–267.

[30]

Fuming Fang, Xin Wang, Junichi Yamagishi, Isao Echizen, Massimiliano Todisco, Nicholas Evans, and Jean-Francois Bonastre. 2019. Speaker anonymization using x-vector and neural waveform models. In The 10th ISCA Speech Synthesis Workshop. 155–160.

[31]

Natasha Fernandes, Mark Dras, and Annabelle McIver. 2019. Generalised differential privacy for text document processing. In The 8th International Conference on Principles of Security and Trust (POST’19). 123–148.

[32]

Natasha Fernandes, Yusuke Kawamoto, and Takao Murakami. 2020. Locality sensitive hashing with extended differential privacy. arXiv preprint, arXiv:2010.09393 (2020).

[33]

Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, and Nathanael Teissier. 2020. Research challenges in designing differentially private text generation mechanisms. arXiv preprint, arXiv:2012.05403 (2020).

[34]

Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020. Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations. In The 13th ACM International Conference on Web Search and Data Mining (WSDM’20). 178–186.

Digital Library

[35]

Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019. Leveraging hierarchical representations for preserving privacy and utility in text. In 19th IEEE International Conference on Data Mining (ICDM’19). 210–219.

[36]

Oluwaseyi Feyisetan and Shiva Kasiviswanathan. 2021. Private release of text embedding vectors. In Proc. of the 1st Workshop on Trustworthy Natural Language Processing. 15–27.

[37]

Sam Fletcher and Md Zahidul Islam. 2019. Decision tree classification with differential privacy: A survey. ACM Computing Surveys 52, 4 (2019), 1–33.

Digital Library

[38]

Andrea Frome, German Cheung, Ahmad Abdulkader, Marco Zennaro, Bo Wu, Alessandro Bissacco, Hartwig Adam, Hartmut Neven, and Luc Vincent. 2009. Large-scale privacy protection in Google street view. In 12th IEEE International Conference on Computer Vision (ICCV’09). 2373–2380.

[39]

Octavian Ganea, Gary Becigneul, and Thomas Hofmann. 2018. Hyperbolic entailment cones for learning hierarchical embeddings. In The 35th International Conference on Machine Learning (ICML’18). 1646–1655.

[40]

Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006. Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In The 23rd International Conference on Machine Learning (ICML’06). 369–376.

Digital Library

[41]

Xiaolan Gu, Ming Li, Li Xiong, and Yang Cao. 2020. Providing input-discriminative protection for local differential privacy. In 36th IEEE International Conference on Data Engineering (ICDE’20). 505–516.

[42]

Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex, Wenqi Wei, and Ling Liu. 2021. Secure and utility-aware data collection with condensed local differential privacy. IEEE Transactions on Dependable and Secure Computing 18, 5 (2021), 2365–2378. DOI:

Digital Library

[43]

Jihun Hamm. 2017. Minimax filter: Learning to preserve privacy from inference attacks. Journal of Machine Learning Research 18, 1 (2017), 4704–4734.

Digital Library

[44]

Yaowei Han, Sheng Li, Yang Cao, Qiang Ma, and Masatoshi Yoshikawa. 2020. Voice-indistinguishability: Protecting voiceprint in privacy-preserving speech data release. In 21st IEEE International Conference on Multimedia and Expo (ICME’20). 1–6.

[45]

Muneeb Ul Hassan, Mubashir Husain Rehmani, and Jinjun Chen. 2020. DEAL: Differentially private auction for blockchain-based microgrids energy trading. IEEE Transactions on Services Computing 13, 2 (2020), 263–275.

[46]

Muneeb Ul Hassan, Mubashir Husain Rehmani, and Jinjun Chen. 2020. Differential privacy techniques for cyber physical systems: A survey. IEEE Communications Surveys & Tutorials 22, 1 (2020), 746–789.

Digital Library

[47]

Steven Hill, Zhimin Zhou, Lawrence Saul, and Hovav Shacham. 2016. On the (in) effectiveness of mosaicing and blurring as tools for document redaction. Proceedings on Privacy Enhancing Technologies 2016, 4 (2016), 403–417.

[48]

Naoise Holohan, Spiros Antonatos, Stefano Braghin, and Pól Mac Aonghusa. 2020. The bounded Laplace mechanism in differential privacy. Journal of Privacy and Confidentiality 10, 1 (2020), 1–15. DOI:

[49]

Honglu Jiang, Jian Pei, Dongxiao Yu, Jiguo Yu, Bei Gong, and Xiuzhen Cheng. 2021. Applications of differential privacy in social network analysis: A survey. IEEE Transactions on Knowledge & Data Engineering (TKDE’21).DOI:

[50]

Woohwan Jung, Suyong Kwon, and Kyuseok Shim. 2021. TIDY: Publishing a time interval dataset with differential privacy. IEEE Transactions on Knowledge and Data Engineering (TKDE) 33, 5 (2021), 2280–2294.

[51]

Tadej Justin, Vitomir Štruc, Simon Dobrišek, Boštjan Vesnicer, Ivo Ipšić, and France Mihelič. 2015. Speaker de-identification using diphone recognition and speech synthesis. In The 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG’15). 1–7.

[52]

Parameswaran Kamalaruban, Victor Perrier, Hassan Jameel Asghar, and Mohamed Ali Kaafar. 2020. Not all attributes are created equal: \(d_\mathcal {X}\)-private mechanisms for linear queries. Proceedings on Privacy Enhancing Technologies 2020, 1 (2020), 103–125.

[53]

Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2011. What can we learn privately? SIAM Journal on Computing 40, 3 (2011), 793–826.

Digital Library

[54]

Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra. 2013. Privacy via the Johnson-Lindenstrauss transform. Journal of Privacy and Confidentiality 5, 1 (2013), 39–71.

[55]

Moshe Koppel, Jonathan Schler, and Shlomo Argamon. 2011. Authorship attribution in the wild. Language Resources and Evaluation 45, 1 (2011), 83–94.

Digital Library

[56]

Fragkiskos Koufogiannis and George J. Pappas. 2016. Location-dependent privacy. In 55th IEEE Conference on Decision and Control (CDC’16). 7586–7591.

Digital Library

[57]

Fragkiskos Koufogiannis and George J. Pappas. 2016. Multi-owner multi-user privacy. In 55th IEEE Conference on Decision and Control (CDC’16). 1787–1793.

Digital Library

[58]

Jingjie Li, Amrita Roy Chowdhury, Kassem Fawaz, and Younghyun Kim. 2021. Kal\(\varepsilon\)ido: Real-time privacy control for eye-tracking systems. In The 30th USENIX Security Symposium.

[59]

Tao Li and Chris Clifton. 2021. Differentially private imaging via latent space manipulation. arXiv preprint arXiv: 2103.05472 (2021).

[60]

Ao Liu, Lirong Xia, Andrew Duchowski, Reynold Bailey, Kenneth Holmqvist, and Eakta Jain. 2019. Differential privacy for eye-tracking data. In The 11th ACM Symposium on Eye Tracking Research & Applications (ETRA’19). 28:1–28:10.

[61]

Fang Liu. 2018. Generalized Gaussian mechanism for differential privacy. IEEE Transactions on Knowledge and Data Engineering (TKDE) 31, 4 (2018), 747–756.

Digital Library

[62]

Naurang S. Mangat. 1994. An improved randomized response strategy. Journal of the Royal Statistical Society: Series B (Methodological) 56, 1 (1994), 93–95.

[63]

Richard McPherson, Reza Shokri, and Vitaly Shmatikov. 2016. Defeating image obfuscation with deep learning. arXiv preprint, arXiv:1609.00408 (2016).

[64]

Frank McSherry and Kunal Talwar. 2007. Mechanism design via differential privacy. In The 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07). 94–103.

[65]

Frank D. McSherry. 2009. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In Proc. of the 35th ACM SIGMOD International Conference on Management of Data (SIGMOD’09). 19–30.

Digital Library

[66]

Ricardo Mendes, Mariana Cunha, and João P. Vilela. 2020. Impact of frequency of location reports on the privacy level of geo-indistinguishability. Proceedings on Privacy Enhancing Technologies 2020, 2 (2020), 379–396.

[67]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint, arXiv:1301.3781 (2013).

[68]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proc. of the 27th Annual Conference on Neural Information Processing Systems (NeurIPS’13). 3111–3119.

[69]

Takao Murakami and Yusuke Kawamoto. 2019. Utility-optimized local differential privacy mechanisms for distribution estimation. In The 28th USENIX Security Symposium. 1877–1894.

[70]

A. Nautsch, C. Jasserand, Els Kindt, M. Todisco, I. Trancoso, and N. Evans. 2019. The GDPR & speech data: Reflections of legal and technology communities, first steps towards a common understanding. In Proc. of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH’19). 3695–3699.

[71]

Boel Nelson and Jenni Reuben. 2019. Chasing accuracy and privacy, and catching both: A literature survey on differentially private histogram publication. arXiv preprint, arXiv:1910.14028 (2019).

[72]

Maximillian Nickel and Douwe Kiela. 2017. Poincaré embeddings for learning hierarchical representations. In The 31st Annual Conference on Neural Information Processing Systems (NeurIPS’17). 6338–6347.

[73]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In The 19th Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1532–1543.

[74]

Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, and Raluca Ada Popa. 2020. Visor: Privacy-preserving video analytics as a cloud service. In The 29th USENIX Security Symposium. 1039–1056.

[75]

Lianyong Qi, Xuyun Zhang, Wanchun Dou, Chunhua Hu, Chi Yang, and Jinjun Chen. 2018. A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Future Generation Computer Systems 88 (2018), 636–643.

Digital Library

[76]

Jianwei Qian, Haohua Du, Jiahui Hou, Linlin Chen, Taeho Jung, and Xiangyang Li. 2019. Speech sanitizer: Speech content desensitization and voice anonymization. IEEE Transactions on Dependable and Secure Computing 18, 6 (2019), 2631–2642. DOI:

Digital Library

[77]

Jianwei Qian, Haohua Du, Jiahui Hou, Linlin Chen, Taeho Jung, and Xiang-Yang Li. 2018. Hidebehind: Enjoy voice input with voiceprint unclonability and anonymity. In The 16th ACM Conference on Embedded Networked Sensor Systems (SenSys’18). 82–94.

Digital Library

[78]

Youyang Qu, Shui Yu, Wanlei Zhou, Shiping Chen, and Jun Wu. 2021. Customizable reliable privacy-preserving data sharing in cyber-physical social network. IEEE Transactions on Network Science and Engineering 8, 1 (2021), 269–281.

[79]

Youyang Qu, Shui Yu, Wanlei Zhou, and Yonghong Tian. 2020. GAN-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Transactions on Network Science and Engineering 7, 4 (2020), 2576–2586.

[80]

Vibhor Rastogi and Suman Nath. 2010. Differentially private aggregation of distributed time-series with transformation and encryption. In Proc. of the 36th ACM SIGMOD International Conference on Management of Data (SIGMOD’10). 735–746.

Digital Library

[81]

Mercedes Rodriguez-Garcia, Montserrat Batet, and David Sánchez. 2015. Semantic noise: Privacy-protection of nominal microdata through uncorrelated noise addition. In The 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’15). 1106–1113.

Digital Library

[82]

Mukesh Saini, Pradeep K. Atrey, Sharad Mehrotra, and Mohan Kankanhalli. 2014. W3-privacy: Understanding what, when, and where inference channels in multi-camera surveillance video. Multimedia Tools and Applications 68, 1 (2014), 135–158.

Digital Library

[83]

David Sánchez and Montserrat Batet. 2016. C-sanitized: A privacy model for document redaction and sanitization. Journal of the Association for Information Science and Technology 67, 1 (2016), 148–163.

Digital Library

[84]

Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, and Hanna Wallach. 2019. Locally private Bayesian inference for count models. In The 36th International Conference on Machine Learning. 5638–5648.

[85]

Andrew Senior, Sharath Pankanti, Arun Hampapur, Lisa Brown, Ying-Li Tian, Ahmet Ekin, Jonathan Connell, Chiao Fe Shu, and Max Lu. 2005. Enabling video privacy through computer vision. IEEE Security & Privacy 3, 3 (2005), 50–57.

Digital Library

[86]

Reza Shokri and Vitaly Shmatikov. 2015. Privacy-preserving deep learning. In Proc. of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS’15). 1310–1321.

Digital Library

[87]

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In 38th IEEE Symposium on Security and Privacy (S&P’17). 3–18.

[88]

David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. 2018. x-vectors: Robust DNN embeddings for speaker recognition. In 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’18). 5329–5333.

Digital Library

[89]

Congzheng Song and Vitaly Shmatikov. 2019. Auditing data provenance in text-generation models. In The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’19). 196–206.

Digital Library

[90]

Brij Mohan Lal Srivastava, Nathalie Vauquier, Md Sahidullah, Aurélien Bellet, Marc Tommasi, and Emmanuel Vincent. 2020. Evaluating voice conversion-based privacy protection against informed attackers. In The 45th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20). 2802–2806.

[91]

Nina Mesing Stausholm. 2021. Improved differentially private Euclidean distance approximation. In Proc. of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS’21). 42–56.

[92]

Julian Steil, Inken Hagestedt, Michael Xuelin Huang, and Andreas Bulling. 2019. Privacy-aware eye tracking using differential privacy. In The 11th ACM Symposium on Eye Tracking Research & Applications (ETRA’19). 27:1–27:9.

[93]

Mingxuan Sun, Qing Wang, and Zicheng Liu. 2020. Human action image generation with differential privacy. In The 21st IEEE International Conference on Multimedia and Expo (ICME’20). 1–6.

[94]

Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, and Mario Fritz. 2018. Natural and effective obfuscation by head inpainting. In Proc. of the 21st IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 5050–5059.

[95]

Shun Takagi, Yang Cao, Yasuhito Asano, and Masatoshi Yoshikawa. 2019. Geo-graph-indistinguishability: Protecting location privacy for LBS over road networks. In 33rd IFIP Annual WG 11.3 Conference on Data and Applications Security and Privacy (DBSec’19). 143–163.

Digital Library

[96]

Manolis Terrovitis, Nikos Mamoulis, and Panos Kalnis. 2008. Privacy-preserving anonymization of set-valued data. Proceedings of the VLDB Endowment 1, 1 (2008), 115–125.

Digital Library

[97]

Florian Tramér, Zhicong Huang, Jean-Pierre Hubaux, and Erman Ayday. 2015. Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies. In Proc. of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS’15). 1286–1297.

Digital Library

[98]

Michael Carl Tschantz, Shayak Sen, and Anupam Datta. 2020. SoK: Differential privacy as a causal property. In 41st IEEE Symposium on Security and Privacy (S&P’20). 354–371.

[99]

William Vickrey. 1961. Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance 16, 1 (1961), 8–37.

[100]

Jesús Villalba, Nanxin Chen, David Snyder, Daniel Garcia-Romero, Alan McCree, Gregory Sell, Jonas Borgstrom, Leibny Paola García-Perera, Fred Richardson, Réda Dehak, et al. 2020. State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and speakers in the wild evaluations. Computer Speech & Language 60 (2020), 101026. DOI:

Digital Library

[101]

Sameer Wagh, Xi He, Ashwin Machanavajjhala, and Prateek Mittal. 2021. DP-cryptography: Marrying differential privacy and cryptography in emerging applications. Communications of the ACM 64, 2 (2021), 84–93.

Digital Library

[102]

Isabel Wagner and David Eckhoff. 2018. Technical privacy metrics: A systematic survey. ACM Computing Surveys 51, 3 (2018), 1–38.

Digital Library

[103]

Han Wang, Yuan Hong, Yu Kong, and Jaideep Vaidya. 2020. Publishing video data with indistinguishable objects. In Proc. of the 23rd International Conference on Extending Database Technology (EDBT’20). 323–334.

[104]

Han Wang, Shangyu Xie, and Yuan Hong. 2020. VideoDP: A universal platform for video analytics with differential privacy. Proceedings on Privacy Enhancing Technologies 2020, 4 (2020), 277–296.

[105]

Tao Wang, Zhigao Zheng, Ali Kashif Bashir, Alireza Jolfaei, and Yanyan Xu. 2021. FinPrivacy: A privacy-preserving mechanism for fingerprint identification. ACM Transactions on Internet Technology 21, 3 (2021). DOI:

Digital Library

[106]

Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612.

Digital Library

[107]

Zhibo Wang, Hengchang Guo, Zhifei Zhang, Mengkai Song, Siyan Zheng, Qian Wang, and Ben Niu. 2020. Towards compression-resistant privacy-preserving photo sharing on social networks. In The 21st International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (Mobihoc’20). 81–90.

[108]

Stanley L. Warner. 1965. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60, 309 (1965), 63–69.

[109]

Benjamin Weggenmann and Florian Kerschbaum. 2018. SynTF: Synthetic and differentially private term frequency vectors for privacy-preserving text mining. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’18). 305–314.

Digital Library

[110]

Bingzhe Wu, Shiwan Zhao, Guangyu Sun, Xiaolu Zhang, Zhong Su, Caihong Zeng, and Zhihong Liu. 2019. P3SGD: Patient privacy preserving SGD for regularizing deep CNNs in pathological image classification. In Proc. of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). 2099–2108.

[111]

Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. 2018. Towards privacy-preserving visual recognition via adversarial training: A pilot study. In The 15th European Conference on Computer Vision (ECCV’18). 606–624.

[112]

Zhuolun Xiang, Bolin Ding, Xi He, and Jingren Zhou. 2020. Linear and range counting under metric-based local differential privacy. In 17th IEEE International Symposium on Information Theory (ISIT’20). 908–913.

[113]

Chuan Xu, Li Luo, Yingyi Ding, Guofeng Zhao, and Shui Yu. 2020. Personalized location privacy protection for location-based services in vehicular networks. IEEE Wireless Communications Letters 9, 10 (2020), 1633–1637.

[114]

Nan Xu, Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, and Nathanael Teissier. 2021. Density-aware differentially private textual perturbations using truncated Gumbel noise. Proceedings of FLAIRS 34, 1 (2021), 1–8. DOI:

[115]

Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, and Nathanael Teissier. 2020. A differentially private text perturbation method using a regularized Mahalanobis metric. In Proc. of the 2nd Workshop on PrivateNLP at the 25th Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 7–17.

[116]

Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, and Nathanael Teissier. 2021. On a utilitarian approach to privacy preserving text generation. In Proc. of the 3rd Workshop on Privacy in Natural Language Processing. 11–20.

[117]

Wanli Xue, Dinusha Vatsalan, Wen Hu, and Aruna Seneviratne. 2020. Sequence data matching and beyond: New privacy-preserving primitives based on Bloom filters. IEEE Transactions on Information Forensics and Security (TIFS) 15 (2020), 2973–2987.

[118]

Jimei Yang, Brian Price, Scott Cohen, Honglak Lee, and Ming-Hsuan Yang. 2016. Object contour detection with a fully convolutional encoder-decoder network. In Proc. of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 193–202.

[119]

Qingqing Ye, Haibo Hu, Xiaofeng Meng, and Huadi Zheng. 2019. PrivKV: Key-value data collection with local differential privacy. In 40th IEEE Symposium on Security and Privacy (S&P’19). 317–331.

[120]

Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman S. M. Chow. 2021. Differential privacy for text analytics via natural text sanitization. In The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP).

[121]

Xuyun Zhang, Christopher Leckie, Wanchun Dou, Jinjun Chen, Ramamohanarao Kotagiri, and Zoran Salcic. 2016. Scalable local-recoding anonymization using locality sensitive hashing for big data privacy preservation. In The 25th ACM International on Conference on Information and Knowledge Management (CIKM’16). 1793–1802.

Digital Library

[122]

Tianqing Zhu, Gang Li, Wanlei Zhou, and S. Yu Philip. 2017. Differentially private data publishing and analysis: A survey. IEEE Transactions on Knowledge and Data Engineering (TKDE) 29, 8 (2017), 1619–1638.

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Cited By

Demelius LKern RTrügler A(2025)Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic SurveyACM Computing Surveys10.1145/371200057:6(1-28)Online publication date: 21-Jan-2025
https://dl.acm.org/doi/10.1145/3712000
Ali WZhou XShao J(2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708982
Zhao RZhang YWang TWen WXiang YCao X(2025)Visual Content Privacy Protection: A SurveyACM Computing Surveys10.1145/370850157:5(1-36)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3708501
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Index Terms

A Survey on Differential Privacy for Unstructured Data Content

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 10s

January 2022

831 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551649

Editor:
Albert Zomaya
University of Sydney, Australia

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 September 2022

Online AM: 06 January 2022

Accepted: 25 September 2021

Revised: 22 July 2021

Received: 17 January 2021

Published in CSUR Volume 54, Issue 10s

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Demelius LKern RTrügler A(2025)Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic SurveyACM Computing Surveys10.1145/371200057:6(1-28)Online publication date: 21-Jan-2025
https://dl.acm.org/doi/10.1145/3712000
Ali WZhou XShao J(2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708982
Zhao RZhang YWang TWen WXiang YCao X(2025)Visual Content Privacy Protection: A SurveyACM Computing Surveys10.1145/370850157:5(1-36)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3708501
Laishram LShaheryar MLee JJung S(2025)Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and SolutionsACM Computing Surveys10.1145/367322457:6(1-38)Online publication date: 10-Feb-2025
https://dl.acm.org/doi/10.1145/3673224
Lyu MNi ZChen QLi F(2025)Edge-DPSDG: An Edge-Based Differential Privacy Protection Model for Smart HealthcareIEEE Transactions on Big Data10.1109/TBDATA.2024.336607111:1(21-34)Online publication date: Feb-2025
https://doi.org/10.1109/TBDATA.2024.3366071
Zhang CLiu YXu MYang XLi PYang CLiu QXiong XChen PWang W(2025)Trans-Border Trusted Data Spaces: A General Framework Supporting Trustworthy International Data CirculationIEEE Access10.1109/ACCESS.2025.354129513(30481-30496)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541295
Deng YZhou WHaq AAhmad STabassum A(2025)Differentially private recommender framework with Dual semi-AutoencoderExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125447260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125447
Yogi MChakravarthy A(2025)A novel user centric privacy mechanism in cyber physical systemComputers & Security10.1016/j.cose.2024.104163149(104163)Online publication date: Feb-2025
https://doi.org/10.1016/j.cose.2024.104163
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
Xue HYuan XLiu BDing M(2025)DBFIA: Diffusion-Based Face Image AnonymizationAlgorithms and Architectures for Parallel Processing10.1007/978-981-96-1525-4_3(40-59)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-981-96-1525-4_3
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