Subspace Differential Privacy
Subspace Differential Privacy
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DOI：
10.1609/aaai.v36i4.20315
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Jie Gao;Ruobin Gong;Fang-Yi Yu
中科院分区：
文献类型：
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作者：
Jie Gao;Ruobin Gong;Fang-Yi Yu
由于实际需要，许多数据应用都有一定的不变量约束。采用差异隐私的数据管理员需要尊重对已消毒数据产品的此类限制，并将其作为主要的实用需求。不变量对隐私保证的表述、实现和解释提出了挑战。我们提出了子空间差分隐私，以诚实地表征对数据机密方面的消毒输出的依赖。我们讨论了两种设计框架，它们将众所周知的差分私有机制（如高斯机制和拉普拉斯机制）转换为尊重策展人指定的不变量的子空间差分私有机制。对于线性查询，我们讨论了最小化均方误差的近最优机制的设计。子空间差分私有机制消除了由于不变量导致的后处理需求，保持了输出的透明性和统计可理解性，适合于分布式实现。我们在2020年人口普查避免披露示范数据和大型大学校园移动接入点连接的时空数据集上展示了提出的机制。
Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation, and interpretation of privacy guarantees. We propose subspace differential privacy, to honestly characterize the dependence of the sanitized output on confidential aspects of the data. We discuss two design frameworks that convert well-known differentially private mechanisms, such as the Gaussian and the Laplace mechanisms, to subspace differentially private ones that respect the invariants specified by the curator. For linear queries, we discuss the design of near-optimal mechanisms that minimize the mean squared error. Subspace differentially private mechanisms rid the need for post-processing due to invariants, preserve transparency and statistical intelligibility of the output, and can be suitable for distributed implementation. We showcase the proposed mechanisms on the 2020 Census Disclosure Avoidance demonstration data, and a spatio-temporal dataset of mobile access point connections on a large university campus.