Optimizing Fitness-For-Use of Differentially Private Linear Queries
Optimizing Fitness-For-Use of Differentially Private Linear Queries
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DOI：
10.14778/3467861.3467864
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Yingtai Xiao;Zeyu Ding;Yuxin Wang;Danfeng Zhang;Daniel Kifer
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文献类型：
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作者：
Yingtai Xiao;Zeyu Ding;Yuxin Wang;Danfeng Zhang;Daniel Kifer
实际上，私有数据发布旨在支持各种应用程序。如果数据发布符合每个应用程序的目标准确性要求，则适合使用。在本文中，我们考虑了在差异隐私下回答线性查询的问题。现有的实用框架（例如矩阵机制）无法提供如此细粒度的控制（它们优化了总误差，这使某些查询答案比必要的更准确，而牺牲了其他不再有用的查询）。因此，我们设计了一种适合用途的策略，该策略添加了保护隐私的高斯噪声以查询答案。优化了噪声的协方差结构，以满足细粒度的精度要求，同时最大程度地减少隐私成本。
In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In this paper, we consider the problem of answering linear queries under differential privacy subject to per-query accuracy constraints. Existing practical frameworks like the matrix mechanism do not provide such fine-grained control (they optimize total error, which allows some query answers to be more accurate than necessary, at the expense of other queries that become no longer useful). Thus, we design a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers. The covariance structure of the noise is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.