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Privacy Analysis ofQuery-Set-Size Control
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AI
DOI：10.1145/3532774.png)
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摘要
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The publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this article, we examine and analyze the privacy offered when using the query-set-size control method for aggregate queries over a data structures representing various topologies. We focus on the mathematical queries of minimum, maximum, median, and average and show some query types that may be used to extract hidden information. We prove some combinations of these queries will maintain a measurable level of privacy even when using multiple queries. We offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system. Our results are mathematically proven and backed by simulations using vehicular network data based on the TAPASCologne project.
Keyword:
Privacy
query-set-size-control
privacy measure
vehicular network
anonymity
期刊
A
IF:
2.8
论文数:
293
被引数:
770
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