scholarly journals On Location Privacy Protection Algorithm Based on Edge Computing and Caching

2021 ◽  
Vol 2138 (1) ◽  
pp. 012010
Author(s):  
Xiaobei Xu ◽  
Huaju Song ◽  
Kai Zhang ◽  
Liwen Chen ◽  
Yuwen Qian
Keyword(s):  
Markov Chain ◽  
Privacy Protection ◽  
Location Privacy ◽  
Transmission Delay ◽  
Mobile Users ◽  
Communication Overhead ◽  
Prediction System ◽  
Hit Rate ◽  
Protection Method ◽  
Location Privacy Protection

Abstract To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.

scholarly journals A MADM Location Privacy Protection Method Based on Blockchain

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 27802-27812
Author(s):  
Hui Wang ◽  
Chengjie Wang ◽  
Zihao Shen ◽  
Kun Liu ◽  
Peiqian Liu ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Protection Method ◽  
Location Privacy Protection

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.

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