research-article

Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

Authors:

Xiangrong Tong,

Guisheng YinAuthors Info & Claims

Volume 135, Issue C

Pages 32 - 43

https://doi.org/10.1016/j.comnet.2018.02.008

Published: 22 April 2018 Publication History

Abstract

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. In order to improve the efficiency and truthfulness of mobile crowdsourcing systems, this paper proposes a truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. The improved two-stage auction algorithm based on trust degree and privacy sensibility (TATP) is proposed. In addition, the k − ɛ-differential privacy-preserving is proposed to prevent users’ location information from being leaked. Through comparison experiments, the effectiveness of the proposed incentive mechanism is verified. The proposed incentive mechanism with location privacy-preserving can inspire users to participate sensing tasks, and protect users’ location privacy effectively.

References

[1]

Z. Duan, W. Li, Z. Cai, Distributed auctions for task assignment and scheduling in mobile crowdsensing systems, The 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta, GA, USA, 2017, pp. 635–644,.

[2]

Y. Liang, Z. Cai, Q. Han, Y. Li, Location privacy leakage through sensory data, Secur. Commun. Netw. J. (2017) 1–12,.

Digital Library

[3]

G. Sun, Y. Xie, D. Liao, H. Yu, V. Chang, User-defined privacy location-sharing system in mobile online social networks, J. Netw. Comput. Appl. 86 (2017) 34–45,.

Digital Library

[4]

Z. He, Z. Cai, J. Yu, Latent-data privacy preserving with customized data utility for social network data, IEEE Trans. Veh. Technol. (2017),.

[5]

J. Li, Z. Cai, M. Yan, Y. Li, Using crowdsourced data in location-based social networks to explore influence maximization, The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 2016, pp. 1–9,.

Digital Library

[6]

G. Sun, V. Chang, Z. Sun, G. Li, H. Yu, D. Liao, Efficient location privacy algorithm for internet of things (IoT) services and applications, J. Netw. Comput. Appl. 89 (2017) 3–13,.

Digital Library

[7]

L. Zhang, Z. Cai, X. Wang, Fake Mask: a novel privacy preserving approach for smartphones, IEEE Trans. Netw. Serv. Manag. 13 (2016) 335–348,.

[8]

J. Li, Z. Cai, J. Wang, M. Han, Y. Li, Truthful incentive mechanisms for geographical position conflicting mobile crowdsensing systems, IEEE Trans. Comput. Social Syst. 99 (2018) 1–11,.

[9]

J.S. Lee, B. Hoh, Sell your experiences: a market mechanism based incentive for participatory sensing, IEEE International Conference on Pervasive Computing and Communications, Mannheim, Germany, 2010, pp. 60–68,.

[10]

Z. Feng, Y. Zhu, Q. Zhang, L.M. Ni, A.V. Vasilakos, TRAC: truthful auction for location-aware collaborative sensing in mobile crowdsourcing, IEEE International Conference on Pervasive Computing and Communications, Mannheim, Germany, 2010, pp. 60–68,.

[11]

I. Krontiris, A. Albers, Monetary incentives in participatory sensing using multi-attributive auctions, Int. J. Parallel Emerg.Distrib. Syst. 27 (2012) 317–336,.

Digital Library

[12]

T. Luo, H.P. Tan, L. Xia, Profit-maximizing incentive for participatory sensing, The 33rd IEEE Conference on Computer Communications, Toronto, ON, Canada, 2014, pp. 127–135,.

[13]

D. Yang, X. Fang, G. Xue, Truthful incentive mechanisms for k-anonymity location privacy, The 32nd IEEE Conference on Computer Communications, Turin, 2013, pp. 2994–3002,.

[14]

L. Gao, F. Hou, J. Huang, Providing long-term participation incentive in participatory sensing, The 34th IEEE Conference on Computer Communications, Kowloon, Hong Kong, 2015, pp. 2803–2811,.

[15]

D. Yang, G. Xue, X. Fang, J. Tang, Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing, The 18th Annual International Conference on Mobile Computing and Networking, Istanbul, Turkey, 2013, pp. 173–184,.

Digital Library

[16]

Y. Singer, M. Mittal, Pricing mechanisms for crowdsourcing markets, The 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1157–1166,.

Digital Library

[17]

X. Chen, M. Liu, Y. Zhou, Z. Li, S. Chen, X. He, A truthful incentive mechanism for online recruitment in mobile crowd sensing system, Sensors 17 (2017) 79,.

[18]

M. Bateni, M. Hajiaghayi, M. Zadimoghaddam, Submodular secretary problem and extensions, ACM Trans. Algorithms 9 (2013) 39–52,.

Digital Library

[19]

D. Zhao, X. Li, H. Ma, How to crowdsource tasks truthfully without sacrificing utility: online incentive mechanisms with budget constraint, The 33rd IEEE Conference on Computer Communications, Toronto, ON, Canada, 2014, pp. 1213–1221,.

[20]

G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, K. Tan, Private queries in location based services: anonymizers are not necessary, The 28th ACM International Conference on Management of Data (SIGMOD), New York, NY, USA, 2008, pp. 121–132,.

Digital Library

[21]

B. Niu, Z. Zhang, X. Li, H. Li, Privacy-area aware dummy generation algorithms for location-based services, Proceedings of 2014 IEEE International Conference on Communications, Sydney, NSW, Australia, 2014, pp. 957–962,.

[22]

B. Gedik, L. Liu, Location privacy in mobile systems: a personalized anonymization model, The 25th IEEE International Conference on Distributed Computing Systems, Washington, DC, USA, 2005, pp. 620–629,.

Digital Library

[23]

M. Fridman, A. Farsi, Y. Okawachi, A.L. Gaeta, Demonstration of temporal cloaking, Nature 481 (2012) 62–65,.

[24]

B. Bamba, L. Liu, Supporting anonymous location queries in mobile environments with privacygrid, The 17th International Conference on World Wide Web (WWW’08), Beijing, China, 2008, pp. 237–246,.

Digital Library

[25]

B. Gedik, L. Liu, Protecting location privacy with personalized k-anonymity: architecture and algorithms, IEEE Trans. Mob. Comput. 7 (2008) 1–18,.

Digital Library

[26]

H. Kido, Y. Yanagisawa, T. Satoh, An anonymous communication technique using dummies for location-based services, IEEE International Conference on Pervasive Services(ICPS’05), Santorini, Greece, 2005, pp. 88–97,.

[27]

X. Zheng, Z. Cai, J. Li, H. Gao, Location-privacy-aware review publication mechanism for local business service systems, The 36th Annual IEEE International Conference on Computer Communications, Atlanta, GA, USA, 2017, pp. 1–9,.

[28]

C. Dwork, M. Naor, T. Pitassi, G.N. Rothblum, Differential privacy under continual observation, The 2010 ACM International Symposium on Theory of Computing, New York, USA, 2010, pp. 715–724,.

Digital Library

[29]

H. To, G. Ghinita, C. Shahabi, A framework for protecting worker location privacy in spatial crowdsourcing, VLDB Endow. 7 (2014) 919–930,.

Digital Library

[30]

R. Dewri, Local differential perturbations: location privacy under approximate knowledge attackers, IEEE Trans. Mob. Comput. 12 (2013) 2360–2372,.

Digital Library

[31]

Y. Wang, Z. Cai, G. Yin, Y. Gao, X. Tong, G. Wu, An incentive mechanism with privacy protection in mobile crowdsourcing systems, Comput. Netw. 102 (2016) 157–171,.

Digital Library

[32]

G. Yin, Y. Wang, Y. Dong, H. Dong, Wright-fisher multi-strategy trust evolution model with white noise for internetware, Expert Syst. Appl. 40 (2013) 7367–7380,.

[33]

C.Z. Zheng, High bids and broke winners, J. Econ. Theory 100 (2001) 129–171,.

[34]

I. Boutsis, V. Kalogeraki, Privacy preservation for participatory sensing data, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego, CA, 2013, pp. 103–113,.

[35]

B. Zhang, C. Liu, J. Lu, Z. Song, Z. Ren, J. Ma, Privacy-preserving QoI-aware participant coordination for mobile crowdsourcing, Comput. Netw. 101 (2016) 29–41,.

Digital Library

Cited By

Chen ZQu ZXiong NLiu ADong MWang TZhang S(2024)UITDE: A UAV-Assisted Intelligent True Data Evaluation Method for Ubiquitous IoT Systems in Intelligent Transportation of Smart CityIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337341125:8(9597-9607)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3373411
Zhang QWang TTao YXu NChen FXie D(2024)Location privacy protection method based on differential privacy in crowdsensing task allocationAd Hoc Networks10.1016/j.adhoc.2024.103464158:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.adhoc.2024.103464
Meng LWang YWang HTong XSun ZCai Z(2023)Task offloading optimization mechanism based on deep neural network in edge-cloud environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00450-612:1Online publication date: 10-May-2023
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Index Terms

Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy

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

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Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 135, Issue C

Apr 2018

302 pages

ISSN:1389-1286

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 22 April 2018

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Cited By

Chen ZQu ZXiong NLiu ADong MWang TZhang S(2024)UITDE: A UAV-Assisted Intelligent True Data Evaluation Method for Ubiquitous IoT Systems in Intelligent Transportation of Smart CityIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337341125:8(9597-9607)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3373411
Zhang QWang TTao YXu NChen FXie D(2024)Location privacy protection method based on differential privacy in crowdsensing task allocationAd Hoc Networks10.1016/j.adhoc.2024.103464158:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.adhoc.2024.103464
Meng LWang YWang HTong XSun ZCai Z(2023)Task offloading optimization mechanism based on deep neural network in edge-cloud environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00450-612:1Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1186/s13677-023-00450-6
Guo JNi QWu WDu D(2023)Multi-Task Diffusion Incentive Design for Mobile Crowdsourcing in Social NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.331038323:5(5740-5754)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3310383
Ng JLim WXiong ZCao XNiyato DLeung CKim D(2022)A Hierarchical Incentive Design Toward Motivating Participation in Coded Federated LearningIEEE Journal on Selected Areas in Communications10.1109/JSAC.2021.312605740:1(359-375)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/JSAC.2021.3126057
Kong LWang LGong WYan CDuan YQi L(2022)LSH-aware multitype health data prediction with privacy preservation in edge environmentWorld Wide Web10.1007/s11280-021-00941-z25:5(1793-1808)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11280-021-00941-z
Zheng XYuan QWang BZhang L(2022)A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks AllocationWireless Personal Communications: An International Journal10.1007/s11277-022-09767-y126:1(719-740)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11277-022-09767-y
Zheng XShah SNawaf LRana OZhu YGan J(2022)Cooperative Offloading Based on Online Auction for Mobile Edge ComputingWireless Algorithms, Systems, and Applications10.1007/978-3-031-19211-1_51(617-628)Online publication date: 24-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-19211-1_51
Guo JNi QDing X(2022)A Decentralized Auction Framework with Privacy Protection in Mobile CrowdsourcingAlgorithmic Aspects in Information and Management10.1007/978-3-031-16081-3_18(207-218)Online publication date: 13-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-16081-3_18
Sun ZWang YCai ZLiu TTong XJiang N(2021)A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcingInternational Journal of Intelligent Systems10.1002/int.2237136:5(2058-2080)Online publication date: 31-Mar-2021
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