Abstract
Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.
Highlights
In recent years, location-based service has become a crucial part of our daily activities
(2) We introduce Improved Multi-Objective Particle Swarm Optimization (IMOPSO) for optimizing the anonymous edge set searching in location semantic road network, and run that algorithm on the candidate anonymous edge set iteratively until the threshold value of iterations is met, after which the final anonymous edge set is designated by calculating the fitness function
PRIVACY METRIC To thoroughly analyze the resilience of our proposed privacy scheme, we introduce two privacy metrics based on the semantic relativity attack defined in Section III Part C, one of which is the sum of semantic relative distances denoted by sumdist, for summing up the values of the semantic distances between pairs of anonymous edges in the final anonymous edge set
Summary
Location-based service has become a crucial part of our daily activities. Based on the above observation, we aim to fill the gap in semantic-relativity location privacy protection and achieve an automatic optimal trade-off between the privacy and the utility of the location privacy protection method To this end, we first propose a new attack called semantic-relativity attack (SRA), with which an attacker can infer the user’s position by considering prior knowledge of location semantics of the road network. An improved semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is designed to simultaneously resist semantic-relativity attacks while achieving an optimal tradeoff between privacy and utility. 3. To find the optimal solution with a balanced trade-off between privacy and utility, we adapt multi-objective Particle Swarm Optimization to the road network scenario for generating the final anonymous edge set.
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