Privacy-preserving location-based traffic density monitoring
Author:
Affiliation:
1. School of Information Science and Engineering, Shandong Normal University, Jinan, People's Republic of China
2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, People's Republic of China
Funder
Natural Science Foundation of Shandong Province
Natural Science Foundation of China
Major Scientific and Technological Innovation Project of Shandong Province
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/09540091.2021.1993137
Reference46 articles.
1. An Efficient Approach for Privacy-Preserving of the Client’s Location and Query in M-Business Supplying LBS Services
2. Alsaawy, Y., Alkhodre, A., Eassa, F. A. & Abi Sen, A. A. (2019, March 13–15). Triple cache approach for preserving privacy and enhancing performance of LBS. 6th International conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India. IEEE.
3. Andrés, M. E., Bordenabe, N. E., Chatzikokolakis, K. & Palamidessi, C. (2013, November). Geo-indistinguishability: Differential privacy for location-based systems. Proceedings of the 2013 ACM SIGSAC conference on Computer & Communications Security, CCS '13 (pp. 901–914). ACM. https://doi.org/10.1145/2508859.2516735
4. Chen, S. & Shen, H. (2016, August 23–26). Semantic-aware dummy selection for location privacy preservation. IEEE Trustcom/BigDataSE/ISPA, Tianjin, China (pp. 752–759). IEEE Press. https://doi.org/10.1109/TrustCom.2016.0135
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