Privacy protection quantitative analysis method in location-based service

Abstract

The invention relates to a privacy protection quantitative analysis method in a location-based service. Firstly, an optimal strategy model of user location-based service privacy protection is designed and built; according to corresponding design, an attacker privacy attack strategy model is built then; and, finally, based on game theory thinking, a simulation test is performed aiming at the optimal strategy model of the user location-based service privacy protection and the attacker privacy attack strategy model, the verification analysis is performed aiming at a simulation result, evaluation aiming at a user location-based service privacy protection effect is achieved, a user's specific mobile behavior is analyzed aiming at an attacker by utilizing user historical trajectory data, a condition that a user uses a location privacy protection algorithm is known, and evaluation aiming at user's privacy protection effects of multiple positions in a period of time can be achieved well.

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 security protection. Background technique [0002] With the rapid development of the mobile Internet, many location-based service applications have emerged. For example, the popular WeChat, users only need to provide precise locations to find nearby friends. However, frequently exposing locations to obtain location service requests will personal privacy is threatened. Aiming at the leakage of user location privacy, 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 mechanism is not mature enough. The attacker masters the privacy protection mechanism (LP...

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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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