This paper analyzes a novel method for publishing data while still protecting privacy. The method is based on computing weights that make an existing dataset, for which there are no confidentiality issues, analogous to the dataset that must be kept private. The existing dataset may be genuine but public already, or it may be synthetic. The weights are importance sampling weights, but to protect privacy, they are regularized and have noise added. The weights allow statistical queries to be answered approximately while provably guaranteeing differential privacy. We derive an expression for the asymptotic variance of the approximate answers. Experiments show that the new mechanism performs well even when the privacy budget is small, and when the public and private datasets are drawn from different populations.

中文翻译：

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基于重要性加权的差分隐私

本文分析了一种在保护隐私的同时发布数据的新方法。该方法基于计算权重，使现有数据集不存在机密性问题，类似于必须保密的数据集。现有数据集可能是真实的但已经公开，也可能是合成的。权重是重要性采样权重，但为了保护隐私，它们被正则化并添加了噪声。权重允许近似回答统计查询，同时可证明保证差异隐私。我们推导出近似答案的渐近方差的表达式。实验表明，即使在隐私预算很小，并且公共和私人数据集来自不同的人群时，新机制也能很好地执行。