Research Article
Achieve Efficient and Privacy-Preserving Proximity Detection Scheme for Social Applications
@INPROCEEDINGS{10.1007/978-3-319-78813-5_17, author={Fengwei Wang and Hui Zhu and Rongxing Lu and Fen Liu and Cheng Huang and Hui Li}, title={Achieve Efficient and Privacy-Preserving Proximity Detection Scheme for Social Applications}, proceedings={Security and Privacy in Communication Networks. 13th International Conference, SecureComm 2017, Niagara Falls, ON, Canada, October 22--25, 2017, Proceedings}, proceedings_a={SECURECOMM}, year={2018}, month={4}, keywords={Location-based social application Proximity detection Privacy-preserving Convex polygon spatial search}, doi={10.1007/978-3-319-78813-5_17} }- Fengwei Wang
Hui Zhu
Rongxing Lu
Fen Liu
Cheng Huang
Hui Li
Year: 2018
Achieve Efficient and Privacy-Preserving Proximity Detection Scheme for Social Applications
SECURECOMM
Springer
DOI: 10.1007/978-3-319-78813-5_17
Abstract
This paper proposes an efficient scheme, named CPSS, to perform privacy-preserving proximity detection based on chiphertext of convex polygon spatial search. We consider a scenario where users have to submit their location and search information to the social application server for accessing proximity detection service of location-based social applications (LBSAs). With proximity detection, users can choose any polygon area on the map and search whether their friends are within the select region. Since the location and search information of users are sensitive, submitting these data over plaintext to the social application server raises privacy concerns. Hence, we propose a novel method, with which users can access proximity detection without divulging their search and location information. Specifically, the data of a user is blurred into chipertext in client, thus no one can obtain the sensitive information except the user herself/himself. We prove that the scheme can defend various security threats and validate our scheme using a real LBS dataset. Also, we show that our proposed CPSS is highly efficient in terms of computation complexity and communication overhead.