Location Privacy Protection in Mobile Networks by Xiaolin Li, Xinxin Liu

By Xiaolin Li, Xinxin Liu

This SpringerBrief analyzes the capability privateness threats in instant and cellular community environments, and studies a few latest works. It proposes a number of privateness keeping recommendations opposed to various kinds of privateness threats which are focusing on clients in a cellular community atmosphere. counting on the community structure, various ways will be followed. the 1st proposed process considers a three-party method structure the place there's a depended on valuable authority that may be used to guard clients? privateness. the second one process considers a unconditionally allotted surroundings the place clients practice privateness safety by way of themselves. ultimately, extra basic method structure is mentioned together with how a semi-trusted server may possibly exist, yet clients have to collaborate to accomplish maximized privateness safety. This short is designed for researchers and pros operating with privateness renovation, cellular networks, and danger types. the range of techniques provided makes it precious for college kids besides.

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393–412 (2006) 6. : A peer-to-peer spatial cloaking algorithm for anonymous location-based service. In: Proceedings of GIS (2006) 7. : A formal model of obfuscation and negotiation for location privacy. IEEE Pervasive Comput. (2005) 8. : The inference problem: a survey. ACM SIGKDD Explor. Newsl. 4(2), 6–11 (2002) 9.

Since it is difficult, and sometimes even impractical to deploy a CA with complete information about all the users in various LBS systems, we tackle the problem of privacy protection in mobile networks from a new angle. In this chapter, we propose a distributed dummy user generation method to grant users control over their own privacy protections. We let users generate dummies with similar moving patterns as their own according to their diverse privacy needs at different places. Considering the fact that self-interested users may not be sufficiently motivated to generate dummies due to the high cost of dummy generation using mobile devices, we employ game theory to analyze the non-cooperative behavior of LBS users, and identify the equilibrium solutions.

As LBS evolves, privacy concern becomes more and more important. Protecting location privacy is usually considered as the practice to prevent others from learning a person’s past and future locations [3]. To prevent curious eavesdroppers and adversarial third-party applications from learning a user’s activities, pseudonyms, instead of real identities, are typically used to camouflage users’ location information. Recent works [23, 29] have discovered that even sporadic location information under the protection of pseudonyms is subject to privacy threats.

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