用Wasserstein距离设计隐私数据市场，实现高效定价与采购。
Wasserstein Markets for Differentially-Private Data
- 基于Wasserstein距离构建隐私数据估值机制。
- 可处理数据价值的组合特性，支持任务无关与特定任务采购。
- 将机制转化为可求解的优化问题，适合实际部署。
数据在各行业决策中日益重要，但数据访问引发隐私担忧，需采用差分隐私等保护技术。数据市场可促进数据共享并平衡隐私与效用。现有框架或依赖可信第三方进行高成本估值，或无法捕捉数据价值的组合特性，且未能内生建模差分隐私的影响。本文提出基于Wasserstein距离的差分隐私数据估值机制，并结合激励机制设计理论，构建适用于任务无关数据采购及任务特定采购联合优化的采购机制。这些机制被重构成可计算的混合整数二次锥规划问题，并通过数值实验验证其有效性。
原文摘要 · Abstract (English)
Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted third party to perform computationally expensive valuations or are unable to capture the combinatorial nature of data value and do not endogenously model the effect of differential privacy. This paper addresses these shortcomings by proposing a valuation mechanism based on the Wasserstein distance for differentially-private data, and corresponding procurement mechanisms by leveraging incentive mechanism design theory, for task-agnostic data procurement, and task-specific procurement co-optimisation. The mechanisms are reformulated into tractable mixed-integer second-order cone programs, which are validated with numerical studies.
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