A quantitative analysis method for privacy protection in location services

Abstract

The invention relates to a quantitative analysis method for privacy protection in location services. Firstly, an optimal strategy model for user location service privacy protection is designed and constructed, and then an attacker privacy attack strategy model is designed and constructed accordingly. Finally, based on game theory ideas, the user location The optimal policy model of service privacy protection and the attacker's privacy attack strategy model are simulated, and the simulation results are verified and analyzed to realize the evaluation of the user's location service privacy protection effect, and the attacker uses the user's historical trajectory data to analyze the user's specific Mobile behavior, and mastering the location privacy protection algorithm used by the user, can well realize the evaluation of the privacy protection effect of the user in multiple locations within a period of time.

Description

Technical field [0001] The invention relates to a quantitative analysis method for privacy protection in location services, and belongs to the technical field of network privacy safety protection. Background technique [0002] With the rapid development of the mobile Internet, many applications based on location services have emerged. For example, the popular WeChat. Users only need to provide accurate locations to find nearby friends. However, frequent exposure of locations to obtain location service requests will Make personal privacy threatened. In response to the problem of user location privacy leakage, researchers have proposed a variety of location privacy protection mechanisms (LPPM: Location Privacy Protection Mechanism). [0003] At present, the main research content of location-based services (LBS) is privacy protection technology, but the evaluation of the effectiveness of privacy protection mechanisms is not mature and complete. The attacker masters the privacy prote...

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Problems solved by technology

[0005] However, previous work in the prior art did not consider the background knowledge of the attacker. Later, location privacy quantification tools appeared in the prior art, assuming that the attacker can extract user mobility models, location-based services (LBS) access mode, which is used to infer private information such as user identity, and use the attacker’s expected estimation error to judge the degree of privacy protection; among them, the background knowledge of the attacker is first described in detail: (1) The attacker knows the privacy protection mechanism (LPPM ) specific algorithm; (2) the mobile model of the user; then formalize the optimal goal of the user and the optimal goal of the attacker into a game theory problem, and finally obtain the user’s optimal location privacy protection by solving the optimization problem Strategies and the attacker's optimal attack strategy

However, the current location privacy measurement methods mainly focus on the user's location privacy measurement at a certain moment, and relatively little evaluation of the user's location privacy protection effect over a period of time, and lack of consideration of the background knowledge of strong attackers. The attacker can link multiple location information of the user in chronological order, so as to obtain the probability of the user's location transfer within a period of time

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