计算机科学
服务器
计算机网络
架空(工程)
隐藏物
基于位置的服务
移动边缘计算
信息隐私
计算机安全
操作系统
作者
Shiwen Zhang,Biao Hu,Wei Liang,Kuan‐Ching Li,Brij B. Gupta
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-13
卷期号:10 (11): 9768-9781
被引量:29
标识
DOI:10.1109/jiot.2023.3235707
摘要
Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from an LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual ${K}$ -anonymous (CDKA) location privacy-preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multilevel caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost, and the cache hit ratio to evaluate our work against existing constructions. The results are twofold: our work possesses a better response rate down to 15–32.6 ms and exhibits lower communication cost requirements down to 6.2–38.9 kB compared to existing works. Our scheme witnesses a higher cache hit ratio of up to 13.6% and 39.1% compared to the literature.
科研通智能强力驱动
Strongly Powered by AbleSci AI