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Novel Framework to Preserve Location Information Privacy

Novel Framework to Preserve Location Information Privacy

Researchers from University of Science and Technology Beijing used game theory to protect users against private information leakage in location based services

Location-based services (LBSs) can be used in various applications such as receiving alerts, such as notification of a sale on gas or warning of a traffic jam, locating people on a map displayed on the mobile phone, turn-by-turn navigation to any address and games where your location is part of the game play. However, these services also share location information with untrusted third-parties. Moreover, susceptible mobile social network, lack of users’ awareness regarding privacy protection, and untrusted LBS providers are some of the major factors that lead to data leakage. Technical defects and interest temptations are two major reasons for the leakage of user privacy information in the network.

Now, a team of researchers from University of Science and Technology Beijing suggested a privacy protection model to effectively comprehend the intrinsic nature of the user’s location privacy protection strategy. The framework is based on game theory – the study of mathematical models of strategic interaction between rational decision-makers – and offers LBS users and their potential opponents an approach to negotiate with one another in relation to the access of the private information of the user based on gaming aspects and incentives. The framework allows discloses the user’s trajectory to a certain extent and rejects access in cases where the disclosure of privacy is about to happen.

The game theoretic model develops the possibility that the visitor’s access is authentic by taking into account the current access situation and making use of collected private data that is associated with historical access traces. The team studied conventional game theory aspects such as the existence of a Nash Equilibrium solution in a finite strategic game. In experiments to obtain results, the team used a random approach in which the owner of the private information randomly decides whether to allow the request of the visitor to access their private information to some extent. The team found that the proposed model can protect user privacy better than the conventional privacy protection models. The research was published in the journal MDPI Sensors on April 1, 2019.


Kavya Borgaonkar
Kavya Borgaonkar,

Kavya Borgaonkar
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