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A personalized trajectory privacy protection method
Computers & Security, 2021Abstract Trajectory data of sports or activities are usually collected and shared into social apps like Wechat moments, Sina weibo in public to provide health services and recommendation, while a large number of friends with weak ties in social circle will cause privacy leakage of users’ locations and life habits. To solve the problem, a personalized
Jiachun Li, Guoqian Chen
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Protecting Privacy in Trajectories with a User-Centric Approach
ACM Transactions on Knowledge Discovery from Data, 2018The increased use of location-aware devices, such as smartphones, generates a large amount of trajectory data. These data can be useful in several domains, like marketing, path modeling, localization of an epidemic focus, and so on. Nevertheless, since trajectory information contains personal mobility data, improper use or publication of trajectory ...
Cristina Romero-Tris, David Megías 0001
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Trajectory Privacy Protection Method Based on Differential Privacy in Crowdsensing
IEEE Transactions on Services ComputingDong Xie, Chuanxin Zhao, Fulong Chen
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Trajectory Privacy Protection on Spatial Streaming Data with Differential Privacy
2018 IEEE Global Communications Conference (GLOBECOM), 2018Continuously sharing user's trajectory data which contain one's location information makes the crowd sensing of the traffic dynamics and mobility trends feasible. This kind of spatial streaming data is beneficial for intelligent transportation but at the risk of disclosing personal privacy, even if it is published in statistical form such as “the ...
Xiang Liu +3 more
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Protecting trajectory privacy: A user-centric analysis
Journal of Network and Computer Applications, 2017Abstract The existing evaluation methods for location privacy protection mechanism (LPPM) focus mainly on location privacy at the time of issuing a location-based query, and rarely consider trajectory privacy from where multiple successive queries are issued.
Hang Shen 0001 +3 more
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A Novel Time-Obfuscated Algorithm for Trajectory Privacy Protection
IEEE Transactions on Services Computing, 2014Location-based services (LBS) which bring so much convenience to our daily life have been intensively studied over the years. Generally, an LBS query processing can be categorized into snapshot and continuous queries which access user location information and return search results to the users.
Yu-Ling Hsueh, Ren-Hung Hwang
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Method of trajectory privacy protection based on restraining trajectory in LBS
International Journal of Information and Communication Technology, 2018With the development of mobile positioning technology, location-based services are becoming more and more widely used in life, but it has produced the security problem of the user's privacy leakage. In this paper, the problem of user trajectory privacy protection in location-based services is introduced, and a method of trajectory privacy protection ...
Zemao Zhao +4 more
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Trajectory Privacy Protection Scheme for Different Travel Modes
2021The problem of trajectory privacy is getting more attention in recent years. It is unreasonable to adopt the same privacy protection scheme in diversified travel modes, since the trajectories have different constraints under different travel modes.
Yanzi Li +3 more
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Protecting Privacy-Sensitive Locations in Trajectories with Correlated Positions
2019 IEEE Global Communications Conference (GLOBECOM), 2019The location privacy issue has become a critical research topic recently. The existing solutions do not solve one typical problem in practice: people may only want to protect certain privacy-sensitive locations among a group of temporal and spatial correlated points in a trajectory.
Bo Liu 0001 +5 more
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User Relationship Privacy Protection on Trajectory Data
2019Various mobile devices facilitate the users’ life, but the issues are brought into privacy focus by the individuals. This paper aims at the protection of intimate relationships among users. We consider the intimacy of user relationships based on similar sub-trajectories between the users. Then, we propose a \(k_{mn}\)-anonymity protection model.
Zi Yang, Mingda Yang, Bo Ning 0002
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