scholarly journals A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0182232 ◽  
Author(s):  
Haitao Zhang ◽  
Chenxue Wu ◽  
Zewei Chen ◽  
Zhao Liu ◽  
Yunhong Zhu
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
On Line ◽  
Inference Attacks ◽  
Location Privacy Protection ◽  
Sequence Rules

scholarly journals KLPPS: A k-Anonymous Location Privacy Protection Scheme via Dummies and Stackelberg Game

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Dongdong Yang ◽  
Baopeng Ye ◽  
Wenyin Zhang ◽  
Huiyu Zhou ◽  
Xiaobin Qian
Keyword(s):  
Service Quality ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Stackelberg Game ◽  
Single Point ◽  
Third Party ◽  
Protection Scheme ◽  
Inference Attacks ◽  
Location Privacy Protection

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.

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

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668542 ◽  
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.

scholarly journals A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Meiyu Pang ◽  
Li Wang ◽  
Ningsheng Fang
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Completion Rate ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Computing Environment ◽  
Internet Of Vehicles ◽  
Scheduling Strategy ◽  
Collaborative Scheduling ◽  
Location Privacy Protection

Abstract This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

A Markov Prediction-Based Privacy Protection Scheme for Continuous Query

2019 ◽  
Vol 28 (09) ◽  
pp. 1950147
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Yi Liu ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Mathematical Proof ◽  
Security Analysis ◽  
Continuous Query ◽  
The Real ◽  
Protection Scheme ◽  
Whole Process ◽  
Prediction Scheme ◽  
Location Privacy Protection

The query probability of a location which the user utilizes to request location-based service (LBS) can be used as background knowledge to infer the real location, and then the adversary may invade the privacy of this user. In order to cope with this type of attack, several algorithms had provided query probability anonymity for location privacy protection. However, these algorithms are all efficient just for snapshot query, and simply applying them in the continuous query may bring hazards. Especially that, continuous anonymous locations which provide query probability anonymity in continuous anonymity are incapable of being linked into anonymous trajectories, and then the adversary can identify the real trajectory as well as the real location of each query. In this paper, the query probability anonymity and anonymous locations linkable are considered simultaneously, then based on the Markov prediction, we provide an anonymous location prediction scheme. This scheme can cope with the shortage of the existing algorithms of query probability anonymity in continuous anonymity locations difficult to be linked, and provide query probability anonymity service for the whole process of continuous query, so this scheme can be used to resist the attack of both of statistical attack as well as the infer attack of the linkable. At last, in order to demonstrate the capability of privacy protection in continuous query and the efficiency of algorithm execution, this paper utilizes the security analysis and experimental evaluation to further confirm the performance, and then the process of mathematical proof as well as experimental results are shown.

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