使用范围限制进行合成数据的隐私放大
Privacy Amplification for Synthetic data using Range Restriction
AI总结：
本研究提出了一种基于范围限制的隐私放大方法，通过调整风险加权伪后验机制来增强合成数据的隐私保护。
摘要：
我们介绍了一种新的范围受限的正式数据隐私标准，这些标准基于数据所有者对敏感数据范围的信念进行条件。通过整合这一额外信息，我们可以提供更强的隐私保证（例如放大）。范围受限的正式隐私标准只保护数据值的一部分（或球体），并排除被认为已经公开的范围（或球体）。这些隐私标准旨在用于在渐近差分（aDP）隐私保证下生成合成数据的风险加权伪后验（模型）机制（PPM）。PPM对每个记录的似然贡献按其披露风险成比例地降低权重。PPM在纳入信念的情况下进行调整，通过调整风险加权的伪似然来实现。我们介绍了两种替代调整方法。第一种将数据所有者对敏感范围的知识表示为概率，λ，即从底层生成分布中抽取的数据值落在敏感球体或子空间之外的概率。然后，每个数据点似然贡献中被判定为敏感的部分是（1-λ）≤1，并且是唯一需要风险降低加权的似然部分。第二种调整将知识编码为边缘敏感范围R之间的概率质量差P(R) ≤1。我们使用由此得到的敏感记录的条件（伪）似然，使其最坏情况尾值远离0。我们比较了在aDP和范围受限隐私标准下PPM的隐私和效用属性。
英文摘要：
We introduce a new class of range restricted formal data privacy standards that condition on owner beliefs about sensitive data ranges. By incorporating this additional information, we can provide a stronger privacy guarantee (e.g. an amplification). The range restricted formal privacy standards protect only a subset (or ball) of data values and exclude ranges (or balls) believed to be already publicly known. The privacy standards are designed for the risk-weighted pseudo posterior (model) mechanism (PPM) used to generate synthetic data under an asymptotic Differential (aDP) privacy guarantee. The PPM downweights the likelihood contribution for each record proportionally to its disclosure risk. The PPM is adapted under inclusion of beliefs by adjusting the risk-weighted pseudo likelihood. We introduce two alternative adjustments. The first expresses data owner knowledge of the sensitive range as a probability, $λ$, that a datum value drawn from the underlying generating distribution lies outside the ball or subspace of values that are sensitive. The portion of each datum likelihood contribution deemed sensitive is then $(1-λ) \leq 1$ and is the only portion of the likelihood subject to risk down-weighting. The second adjustment encodes knowledge as the difference in probability masses $P(R) \leq 1$ between the edges of the sensitive range, $R$. We use the resulting conditional (pseudo) likelihood for a sensitive record, which boosts its worst case tail values away from 0. We compare privacy and utility properties for the PPM under the aDP and range restricted privacy standards.
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