Limiting Privacy Breaches in Average-Distance Query

Author:

Xia Huihua1,Xiong Yan1,Huang Wenchao1ORCID,Meng Zhaoyi1,Miao Fuyou1ORCID

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

Abstract

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.

Funder

National Key R&D Program of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Study on Location Based Services and TTP based Privacy Preserving Techniques;2021 International Conference on Advances in Computing and Communications (ICACC);2021-10-21

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