Incentivizing Crowdsensing With Location-Privacy Preserving

Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.

[1]  Xi Fang,et al.  Truthful incentive mechanisms for k-anonymity location privacy , 2013, 2013 Proceedings IEEE INFOCOM.

[2]  Panagiotis Papadimitratos,et al.  Ieee Transactions on Dependable and Secure Computing, Special Issue on " Security and Privacy in Mobile Platforms " , 2014 Hiding in the Mobile Crowd: Location Privacy through Collaboration , 2022 .

[3]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  Lothar Thiele,et al.  Participatory Air Pollution Monitoring Using Smartphones , 2012 .

[5]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[6]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[7]  Lin Gao,et al.  Providing long-term participation incentive in participatory sensing , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[8]  Fan Zhang,et al.  Data perturbation with state-dependent noise for participatory sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  H. Bauchner,et al.  A picture is worth a thousand words , 2002, BMJ : British Medical Journal.

[10]  Miguel A. Labrador,et al.  Preserving privacy while reducing power consumption and information loss in LBS and participatory sensing applications , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[11]  Klara Nahrstedt,et al.  Enabling PrivacyPreserving PrivacyPreserving PrivacyPreserving Incentives for Mobile Crowd Sensing Systems , 2016 .

[12]  Klara Nahrstedt,et al.  INCEPTION: incentivizing privacy-preserving data aggregation for mobile crowd sensing systems , 2016, MobiHoc.

[13]  Xinbing Wang,et al.  A Picture is Worth a Thousand Words: Share Your Real-Time View on the Road , 2017, IEEE Transactions on Vehicular Technology.

[14]  A. Solanas,et al.  V-MDAV : A Multivariate Microaggregation With Variable Group Size , 2006 .

[15]  Josep Domingo-Ferrer,et al.  Practical Data-Oriented Microaggregation for Statistical Disclosure Control , 2002, IEEE Trans. Knowl. Data Eng..

[16]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[17]  Rong Zheng,et al.  Efficient algorithms for K-anonymous location privacy in participatory sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Athanasios V. Vasilakos,et al.  TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[19]  Laurence A. Wolsey,et al.  An analysis of the greedy algorithm for the submodular set covering problem , 1982, Comb..

[20]  Yanchao Zhang,et al.  Privacy-Preserving Crowdsourced Spectrum Sensing , 2018, IEEE/ACM Transactions on Networking.

[21]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[22]  Divesh Srivastava,et al.  Size-Constrained Weighted Set Cover , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[23]  Cyrus Shahabi,et al.  A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing , 2014, Proc. VLDB Endow..

[24]  Xiaohua Tian,et al.  Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing , 2015, IEEE Transactions on Vehicular Technology.

[25]  Marco Gruteser,et al.  USENIX Association , 1992 .

[26]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[27]  Wen Hu,et al.  Preserving privacy in participatory sensing systems , 2010, Comput. Commun..

[28]  George Kokolakis,et al.  Computational Statistics and Data Analysis Importance Partitioning in Micro-aggregation , 2022 .

[29]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[30]  Hui Li,et al.  EPcloak: An Efficient and Privacy-Preserving Spatial Cloaking Scheme for LBSs , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.