差分隐私下效用最大化向量加权机制的重加权
Re-weighting of Vector-weighted Mechanisms for Utility Maximization under Differential Privacy
摘要：
我们解决了实际实施的风险加权伪后验合成器的微数据传播与新的重新加权策略，最大限度地利用发布的合成数据下，在任何级别的正式的隐私保证。我们的重新加权策略适用于任何向量加权的伪后验机制，在该机制下，观察索引权重的向量用于降低高披露风险记录的可能性贡献。我们展示了我们的方法在两个不同的向量加权计划，目标是高风险的记录。我们的新方法，用于构建记录索引的dowschitz最大化的数据效用下的任何隐私预算的向量加权合成器通过调整记录的权重，使他们的个人Lipschitz界限接近整个数据库的界限。我们的方法实现了$(ε= 2 Δ_{\boldsymbolα})-$渐近差分隐私（aDP）的保证，全球范围内，在数据库的空间。我们说明了我们的方法，使用模拟的高度偏斜的计数数据和比较结果的标量加权合成器下的指数机制（EM）。我们还将我们的方法应用于博士学位获得者的调查样本，并证明了我们的方法的实用性。
英文摘要：
We address practical implementation of a risk-weighted pseudo posterior synthesizer for microdata dissemination with a new re-weighting strategy that maximizes utility of released synthetic data under at any level of formal privacy guarantee. Our re-weighting strategy applies to any vector-weighted pseudo posterior mechanism under which a vector of observation-indexed weights are used to downweight likelihood contributions for high disclosure risk records. We demonstrate our method on two different vector-weighted schemes that target high-risk records. Our new method for constructing record-indexed downeighting maximizes the data utility under any privacy budget for the vector-weighted synthesizers by adjusting the by-record weights, such that their individual Lipschitz bounds approach the bound for the entire database. Our method achieves an $(ε= 2 Δ_{\boldsymbolα})-$asymptotic differential privacy (aDP) guarantee, globally, over the space of databases. We illustrate our methods using simulated highly skewed count data and compare the results to a scalar-weighted synthesizer under the Exponential Mechanism (EM). We also apply our methods to a sample of the Survey of Doctorate Recipients and demonstrate the practicality of our methods.
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