Collaborative Location Privacy with Rational Users

Santos, Francisco Eduardo de Luna e Almeida; Humbert, Mathias; Shokri, Reza; Hubaux, Jean-Pierre

doi:10.1007/978-3-642-25280-8_14

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Collaborative Location Privacy Game (CLPG). In [33], it assumes that mobile users don't seek geographic information from the service provider, but get them from the neighbour users. If so, the user has no risk of privacy leakage.…”

Section: Oogmentioning

confidence: 99%

Game Theory for Mobile Location Privacy

Dou

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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With the rapid growth of mobile network and infrastructures, location-related services accross multiple domains have received a great deal of attention. However, privacy is always an important problem that has a great influence on services. In order to research the attack and defence of privacy, we present the existing gametheoretic literatures for their interactions about mobile location privacy problems. We first demonstrate the necessity of game theory applied to location privacy. Then, we divide the literatures into four types according to different game players. Next, we describe the detailed content and analyse the equilibrium of privacy games. In addition, we also provide the works based on mechanism design to motive the defenders to increase the defence in various contexts. Finally, we also discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about location privacy preservation problems in mobile network. CCS CONCEPTS • Security and privacy → Privacy protections; Social aspects of security and privacy; Economics of security and privacy; • Networks → Mobile networks;

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Section: Oogmentioning

confidence: 99%

Game Theory for Mobile Location Privacy

Dou

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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“…This leads to less communication with the service provider because a user contacts the provider only if there are no other users, with the requested information, in range. In this initial work no game theory is used but it is the basis for [12], where Santos et al extend their work by analyzing the collaborative behavior of users in MobiCrowd with gametheoretic methods. The two Nash game equilibria, which they have derived, favor mutual cooperation and mutual defection.…”

Section: Related Workmentioning

confidence: 99%

Game-Theoretic Model of Incentivizing Privacy-Aware Users to Consent to Location Tracking

Panaousis

Lászka

Pohl

et al. 2015

2015 IEEE Trustcom/BigDataSE/Ispa

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Abstract-Nowadays, mobile users have a vast number of applications and services at their disposal. Each of these might impose some privacy threats on users' "Personally Identifiable Information" (PII). Location privacy is a crucial part of PII, and as such, privacy-aware users wish to maximize it. This privacy can be, for instance, threatened by a company, which collects users' traces and shares them with third parties. To maximize their location privacy, users can decide to get offline so that the company cannot localize their devices. The longer a user stays connected to a network, the more services he might receive, but his location privacy decreases. In this paper, we analyze the trade-off between location privacy, the level of services that a user experiences, and the profit of the company. To this end, we formulate a Stackelberg Bayesian game between the User (follower) and the Company (leader). We present theoretical results characterizing the equilibria of the game. To the best of our knowledge, our work is the first to model the economically rational decision-making of the service provider (i.e., the Company) in conjunction with the rational decisionmaking of users who wish to protect their location privacy. To evaluate the performance of our approach, we have used real-data from a testbed, and we have also shown that the game-theoretic strategy of the Company outperforms non-strategic methods. Finally, we have considered different User privacy types, and have determined the service level that incentivizes the User to stay connected as long as possible.

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“…1) relies on a trusted agent, the anonymiser, to collect users' requests and anonymise them before sending them to LBS providers. However, in a distributed implementation users cooperate with each other to construct a generalised region [18,40]. The centralised framework is easy to implement and well-studied in the literature while the distributed framework requires more communications between collaborators and security analysis, e.g., with respect to insiders, is not well studied.…”

Section: Query Privacy and Request Generalisationmentioning

confidence: 99%

Protecting query privacy in location-based services

Chen

Pang

2013

Geoinformatica

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The popularity of location-based services (LBSs) leads to severe concerns on users' privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacypreserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts-user prof iles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users' query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users' query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users' privacy requirements This article is a revised and extended version of our two conference papers [7,8].Xihui Chen is supported by an AFR PhD grant from the National Research Fund, Luxembourg.

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Collaborative Location Privacy with Rational Users

Cited by 12 publications

References 16 publications

Game Theory for Mobile Location Privacy

Game Theory for Mobile Location Privacy

Game-Theoretic Model of Incentivizing Privacy-Aware Users to Consent to Location Tracking

Protecting query privacy in location-based services

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