Protecting location privacy against inference attacks

Protecting location privacy has become an irreversible trend; some problems also come such as system structures adopted by location privacy protection schemes suffer from single point of failure or the mobile device performance bottlenecks, and these schemes cannot resist single-point attacks and inference attacks and achieve a tradeoff between privacy level and service quality. To solve these problems, we propose a k-anonymous location privacy protection scheme via dummies and Stackelberg game. First, we analyze the merits and drawbacks of the existing location privacy preservation system architecture and propose a semitrusted third party-based location privacy preservation architecture. Next, taking into account both location semantic diversity, physical dispersion, and query probability, etc., we design a dummy location selection algorithm based on location semantics and physical distance, which can protect users’ privacy against single-point attack. And then, we propose a location anonymous optimization method based on Stackelberg game to improve the algorithm. Specifically, we formalize the mutual optimization of user-adversary objectives by using the framework of Stackelberg game to find an optimal dummy location set. The optimal dummy location set can resist single-point attacks and inference attacks while effectively balancing service quality and location privacy. Finally, we provide exhaustive simulation evaluation for the proposed scheme compared with existing schemes in multiple aspects, and the results show that the proposed scheme can effectively resist the single-point attack and inference attack while balancing the service quality and location privacy.

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A novel destination prediction attack and corresponding location privacy protection method in geo-social networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147716685421 ◽

2017 ◽

Vol 13 (1) ◽

pp. 155014771668542 ◽

Cited By ~ 7

Author(s):

Di Xue ◽

Li-Fa Wu ◽

Hua-Bo Li ◽

Zheng Hong ◽

Zhen-Ji Zhou

Keyword(s):

Social Networks ◽

Privacy Protection ◽

Location Privacy ◽

Background Information ◽

Data Set ◽

Attack Model ◽

Protection Method ◽

Inference Attacks ◽

Location Privacy Protection ◽

Prediction Approach

Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework.

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GLPP: A Game-Based Location Privacy-Preserving Framework in Account Linked Mixed Location-Based Services

Security and Communication Networks ◽

10.1155/2018/9148768 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Zhuo Ma ◽

Jiuxin Cao ◽

Xiusheng Chen ◽

Shuai Xu ◽

Bo Liu ◽

...

Keyword(s):

Location Privacy ◽

Privacy Preserving ◽

Location Based Services ◽

Behavioral Analysis ◽

Continuous Location ◽

Real Dataset ◽

Privacy Leakage ◽

Cross Platform ◽

Inference Attacks ◽

Zero Sum

In Location-Based Services (LBSs) platforms, such as Foursquare and Swarm, the submitted position for a share or search leads to the exposure of users’ activities. Additionally, the cross-platform account linkage could aggravate this exposure, as the fusion of users’ information can enhance inference attacks on users’ next submitted location. Hence, in this paper, we propose GLPP, a personalized and continuous location privacy-preserving framework in account linked platforms with different LBSs (i.e., search-based LBSs and share-based LBSs). The key point of GLPP is to obfuscate every location submitted in search-based LBSs so as to defend dynamic inference attacks. Specifically, first, possible inference attacks are listed through user behavioral analysis. Second, for each specific attack, an obfuscation model is proposed to minimize location privacy leakage under a given location distortion, which ensures submitted locations’ utility for search-based LBSs. Third, for dynamic attacks, a framework based on zero-sum game is adopted to joint specific obfuscation above and minimize the location privacy leakage to a balanced point. Experiments on real dataset prove the effectiveness of our proposed attacks in Accuracy, Certainty, and Correctness and, meanwhile, also show the performance of our preserving solution in defense of attacks and guarantee of location utility.

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A Method of Sanitizing Privacy-Sensitive Sequence Pattern Networks Mined From Trajectories Released

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2019070104 ◽

2019 ◽

Vol 15 (3) ◽

pp. 63-89

Author(s):

Haitao Zhang ◽

Yunhong Zhu

Keyword(s):

Location Privacy ◽

Mobility Patterns ◽

Sequence Pattern ◽

Multiple Networks ◽

Personalized Services ◽

Node Ratio ◽

Connectivity Degree ◽

Inference Attacks ◽

Optimal Values ◽

Influential Nodes

Mobility patterns mined from released trajectories can help to allocate resources and provide personalized services, although these also pose a threat to personal location privacy. As the existing sanitization methods cannot deal with the problems of location privacy inference attacks based on privacy-sensitive sequence pattern networks, the authors proposed a method of sanitizing the privacy-sensitive sequence pattern networks mined from trajectories released by identifying and removing influential nodes from the networks. The authors conducted extensive experiments and the results were shown that by adjusting the parameter of the proportional factors, the proposed method can thoroughly sanitize privacy-sensitive sequence pattern networks and achieve the optimal values for security degree and connectivity degree measurements. In addition, the performance of the proposed method was shown to be stable for multiple networks with basically the same privacy-sensitive node ratio and be scalable for batches of networks with different sensitive nodes ratios.

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