私有逆Top-k算法在COVID-19国家公共数据中的应用

Mariana M. Silva, Iago C. Chaves, Javam C. Machado
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引用次数: 1

摘要

在本文中,我们提出了一个微分私有的反向top-k查询。我们的策略允许根据搜索标准获得频率较低的数据,并高度保证原始数据库中提供个人数据的个人的隐私。我们使用两种不同的查询将我们的策略应用于纽约州COVID-19的公共数据。我们的实验结果表明,当选择的预算合适时,所提出的top-k查询的结果与传统的top-k查询的结果具有高度的相似性,为研究人员提供了有用的结果,同时确保了由于差分隐私属性而引起的个体重新识别的低概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Private Reverse Top-k Algorithms Applied on Public Data of COVID-19 in the State of Ceará
In this article we propose a differentially private reverse top-k query. Our strategy allows obtaining the less frequent data according to a search criteria, with a high guarantee of privacy of the individuals who contributed with personal data in the original database. We apply our strategy on public data for COVID-19 in the State of Ceará using two different queries. Our experimental results show that the result of the proposed top-k query returns a high degree of similarity to the result of a conventional top-k query, when the chosen budget is suitable, providing useful results for researchers, while ensuring a low probability of re-identification of individuals arising from the properties of differential privacy.
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