强制披露下的合意差分隐私
Congenial Differential Privacy under Mandated Disclosure
摘要：
不同的私有数据发布通常需要满足一系列外部约束，这些约束反映了数据管理者有义务遵守的法律、道德和逻辑要求。强制执行限制，如果被视为后处理，则在私有化数据的生产中增加了一个额外的阶段。在多阶段处理的理论中很好地理解，相性（congeniality）（阶段之间的程序兼容性的一种形式）是最终用户直接获得统计上有效的结果的先决条件。适宜的差分隐私在理论上是有原则的，它促进了机制的透明性和可理解性，否则这些机制将被特设的后处理过程所破坏。我们主张通过标准的概率条件不变的利润率的系统集成的强制披露到隐私机制的设计。条件作用自动地呈现适宜性，因为任何额外的后处理阶段变得不必要。我们提供了初始的理论保证和马尔可夫链算法，我们的建议。我们还讨论了比较先天性差分隐私和基于优化的后处理中出现的有趣的理论问题，以及进一步研究的方向。
英文摘要：
Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is obligated. The enforcement of constraints, when treated as post-processing, adds an extra phase in the production of privatized data. It is well understood in the theory of multi-phase processing that congeniality, a form of procedural compatibility between phases, is a prerequisite for the end users to straightforwardly obtain statistically valid results. Congenial differential privacy is theoretically principled, which facilitates transparency and intelligibility of the mechanism that would otherwise be undermined by ad-hoc post-processing procedures. We advocate for the systematic integration of mandated disclosure into the design of the privacy mechanism via standard probabilistic conditioning on the invariant margins. Conditioning automatically renders congeniality because any extra post-processing phase becomes unnecessary. We provide both initial theoretical guarantees and a Markov chain algorithm for our proposal. We also discuss intriguing theoretical issues that arise in comparing congenital differential privacy and optimization-based post-processing, as well as directions for further research.
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