Abstract
Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure, which incorporates both the spatial distribution and the temporal transition of a user’s activity points. Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.
- Amr Alanwar, Yasser Shoukry, Supriyo Chakraborty, Paul Martin, Paulo Tabuada, and Mani Srivastava. 2017. PrOLoc: Resilient localization with private observers using partial homomorphic encryption. In Proceedings of the ACM/IEEE International Conference on Information Processing in Sensor Networks. 41--52.Google Scholar
Digital Library
- Claudio A. Ardagna, Marco Cremonini, Sabrina De Capitani di Vimercati, and Pierangela Samarati. 2011. An obfuscation-based approach for protecting location privacy. IEEE Transactions on Dependable and Secure Computing 8, 1 (2011), 13--27.Google Scholar
Digital Library
- Ioannis Boutsis and Vana Kalogeraki. 2013. Privacy preservation for participatory sensing data. In Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications. 103--113.Google Scholar
Cross Ref
- A. J. Bernheim Brush, John Krumm, and James Scott. 2010. Exploring end user preferences for location obfuscation, location-based services, and the value of location. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 95--104.Google Scholar
Digital Library
- Mark de Berg, Otfried Cheong, Marc van Kreveld, and Mark Overmars. 2008. Computational Geometry: Algorithms and Applications (3rd ed.). Springer-Verlag.Google Scholar
Digital Library
- B. Gedik and L. Liu. 2008. Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Transactions on Mobile Computing 7, 1 (2008), 1--18.Google Scholar
Digital Library
- Hans U. Gerber and Gérard Pafumi. 1998. Utility functions: From risk theory to finance. North American Actuarial Journal 2, 3 (1998), 74--100.Google Scholar
Cross Ref
- Xiaowen Gong, Xu Chen, Kai Xing, Dong-Hoon Shin, Mengyuan Zhang, and Junshan Zhang. 2017. From social group utility maximization to personalized location privacy in mobile networks. IEEE/ACM Transactions on Networking 25, 3 (2017), 1703--1716.Google Scholar
Digital Library
- Marco Gruteser and Dirk Grunwald. 2003. Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services. 31--42.Google Scholar
Digital Library
- Karmeshu. 2003. Entropy Measures, Maximum Entropy Principle and Emerging Applications. Springer.Google Scholar
- John Kah-Soon Lau, Chen-Khong Tham, and Tie Luo. 2011. Participatory cyber physical system in public transport application. In Proceedings of the 4th IEEE International Conference on Utility and Cloud Computing. 355--360.Google Scholar
- Tie Luo, Salil S. Kanhere, Sajal K. Das, and Hwee-Pink Tan. 2016. Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests. IEEE Transactions on Mobile Computing 15, 9 (2016), 2234--2246.Google Scholar
Digital Library
- Rim Ben Messaoud, Nouha Sghaier, Mohamed Ali Moussa, and Yacine Ghamri-Doudane. 2019. Privacy preserving utility-aware mechanism for data uploading phase in participatory sensing. IEEE Transactions on Mobile Computing 18, 9 (2019), 2160--2173.Google Scholar
Cross Ref
- Ben Niu, Qinghua Li, Xiaoyan Zhu, Guohong Cao, and Hui Li. 2014. Achieving k-anonymity in privacy-aware location-based services. In Proceedings of the IEEE Conference on Computer Communications. 754--762.Google Scholar
Cross Ref
- Hossein Shafagh, Anwar Hithnawi, and Lukas Burkhalter. 2017. Secure sharing of partially homomorphic encrypted IoT data. In Proceedings of the ACM International Conference on Embedded Networked Sensor Systems. Article 29, 14 pages.Google Scholar
Digital Library
- Hossein Shafagh, Anwar Hithnawi, Andreas Droescher, Simon Duquennoy, and Wen Hu. 2015. Talos: Encrypted query processing for the Internet of Things. In Proceedings of the ACM International Conference on Embedded Networked Sensor Systems. 197--210.Google Scholar
Digital Library
- Chen Khong Tham and Tie Luo. 2015. Quality of contributed service and market equilibrium for participatory sensing. IEEE Transactions on Mobile Computing 14, 4 (2015), 829--842.Google Scholar
Digital Library
- Imdad Ullah, Roksana Boreli, Salil S. Kanhere, and Sanjay Chawla. 2014. ProfileGuard: Privacy preserving obfuscation for mobile user profiles. In Proceedings of the Workshop on Privacy in the Electronic Society. 83--92.Google Scholar
Digital Library
- Joel M. Vanden. 2015. General properties of isoelastic utility economies. Mathematical Finance 25, 1 (2015), 187--219.Google Scholar
Cross Ref
- Fang-Jing Wu. 2018. A sensor-assisted emergency guiding system: Sensor-centric or user-centric? IEEE Transactions on Vehicular Technology 67, 2 (2018), 1598--1611.Google Scholar
Cross Ref
- Fang-Jing Wu, Matthias R. Brust, Yan-Ann Chen, and Tie Luo. 2016. The privacy exposure problem in mobile location-based services. In Proceedings of the 2016 IEEE Global Communications Conference.Google Scholar
Cross Ref
- Fang-Jing Wu and Tie Luo. 2015. Infrastructureless signal source localization using crowdsourced data for smart-city applications. In Proceedings of the 2015 IEEE International Conference on Communications. 586--591.Google Scholar
Cross Ref
- Dingqi Yang, Daqing Zhang, Bingqing Qu, and Philippe Cudré-Mauroux. 2016. PrivCheck: Privacy-preserving check-in data publishing for personalized location based services. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 545--556.Google Scholar
Digital Library
Index Terms
CrowdPrivacy: Publish More Useful Data with Less Privacy Exposure in Crowdsourced Location-Based Services
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