scholarly journals Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures

2017 ◽  
Vol 3 (2) ◽  
pp. 255-266 ◽  
Author(s):  
Mohamed Grissa ◽  
Attila A. Yavuz ◽  
Bechir Hamdaoui
Keyword(s):  
Data Structures ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Cognitive Networks ◽  
Probabilistic Data

Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing

2019 ◽  
Vol 23 (5) ◽  
pp. 1167-1185
Author(s):  
Xiaohan Wang ◽  
Yonglong Luo ◽  
Shiyang Liu ◽  
Taochun Wang ◽  
Huihui Han
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Locality Sensitive Hashing

scholarly journals A K-Anonymity Based Schema for Location Privacy Preservation

2019 ◽  
Vol 4 (2) ◽  
pp. 156-167 ◽  
Author(s):  
Fan Fei ◽  
Shu Li ◽  
Haipeng Dai ◽  
Chunhua Hu ◽  
Wanchun Dou ◽  
...  
Keyword(s):  
Privacy Preservation ◽  
Location Privacy

BloomyCAN: Probabilistic Data Structures for Software-defined Controller Area Networks

2021 ◽  
Author(s):  
Dennis Grewe ◽  
Naresh Nayak ◽  
Deeban Babu ◽  
Wenwen Chen ◽  
Sebastian Schildt ◽  
...  
Keyword(s):  
Data Structures ◽  
Probabilistic Data ◽  
Controller Area Networks

scholarly journals Decentralized collaborative TTP free approach for privacy preservation in location based services

2019 ◽  
Vol 9 (6) ◽  
pp. 5376
Author(s):  
Ajaysinh Devendrasinh Rathod ◽  
Saurabh Shah ◽  
Vivaksha J. Jariwala
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Emergency Services ◽  
Personal Information ◽  
Personal Digital Assistant ◽  
Minimum Cost ◽  
Location Based Services ◽  
Third Party ◽  
Trusted Third Party ◽  
Recent Trends

In recent trends, growth of location based services have been increased due to the large usage of cell phones, personal digital assistant and other devices like location based navigation, emergency services, location based social networking, location based advertisement, etc. Users are provided with important information based on location to the service provider that results the compromise with their personal information like user’s identity, location privacy etc. To achieve location privacy of the user, cryptographic technique is one of the best technique which gives assurance. Location based services are classified as Trusted Third Party (TTP) & without Trusted Third Party that uses cryptographic approaches. TTP free is one of the prominent approach in which it uses peer-to-peer model. In this approach, important users mutually connect with each other to form a network to work without the use of any person/server. There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability.  In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability.

scholarly journals A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

2021 ◽  
Author(s):  
Xiaodong Zheng ◽  
Qi Yuan ◽  
Bo Wang ◽  
Lei Zhang
Keyword(s):  
Experimental Verification ◽  
Task Allocation ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Whole Process ◽  
Privacy Violation

Abstract In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

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