设计隐私保护数据发布机制，让合作者能恢复数据，而单个攻击者无法破解。
Minimax Data Sanitization with Distortion Constraint and Adversarial Inference
- 构建双敌手对抗框架，通过最小化最大损失实现隐私保护
- 在高斯与二元场景下获得理论最优解作为基准
- 适合需要协作恢复但防单点泄露的隐私敏感场景
研究一种隐私保护数据共享场景：私有数据经由隐私化处理器转换为可公开的清洗版本，供授权重建者及两个未经授权的攻击者使用，每个攻击者均拥有与原始数据相关的侧信息。重建者依据失真函数评估，每个攻击者则使用独立损失函数评估。隐私化处理器确保重建失真低于固定阈值，同时最大化两个攻击者中的最小损失。该双攻击者设置模拟用户单独无法准确重建数据，但联合侧信息可达到失真阈值的情况。隐私化处理器在保证个体损失最大化的同时，仅允许通过协作实现准确重建。这类似于秘密共享，但采用有损恢复而非完美恢复。我们将其建模为带失真约束的数据驱动极小极大优化问题，并提出交替更新隐私化处理器、重建者和攻击者的数据驱动训练方法。还分析了高斯和二元情况下的特例，获得可解析的最优解，作为所提极小极大训练方法的评估基准。
原文摘要 · Abstract (English)
We study a privacy-preserving data-sharing setting where a privatizer transforms private data into a sanitized version observed by an authorized reconstructor and two unauthorized adversaries, each with access to side information correlated with the private data. The reconstructor is evaluated under a distortion function, while each adversary is evaluated using a separate loss function. The privatizer ensures the reconstructor distortion remains below a fixed threshold while maximizing the minimum loss across the two adversaries. This two-adversary setting models cases where individual users cannot reconstruct the data accurately, but their combined side information enables estimation within the distortion threshold. The privatizer maximizes individual loss while permitting accurate reconstruction only through collaboration. This echoes secret-sharing principles, but with lossy rather than perfect recovery. We frame this as a constrained data-driven minimax optimization problem and propose a data-driven training procedure that alternately updates the privatizer, reconstructor, and adversaries. We also analyze the Gaussian and binary cases as special scenarios where optimal solutions can be obtained. These theoretical optimal results are benchmarks for evaluating the proposed minimax training approach.
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