首个实现差分隐私的Wasserstein均值计算方法，保护敏感数据隐私。
Differentially Private Wasserstein Barycenters
- 基于最优传输距离定义隐私保护的分布均值
- 在合成数据、MNIST和美国人口数据上验证高精度与强隐私性
- 适用于需保护数据隐私的机器学习与统计分析场景
Wasserstein均值是在最优传输度量下定义的一组概率测度的均值，在机器学习、统计学和计算机图形学中有广泛应用。实际中这些输入测度是由敏感数据构建的经验分布，因此需要差分隐私（DP）处理。本文首次提出在差分隐私下计算Wasserstein均值的算法。在合成数据、MNIST及大规模美国人口数据集上，我们的方法生成了高质量的私有均值，实现了优异的准确率-隐私权衡。
原文摘要 · Abstract (English)
The Wasserstein barycenter is defined as the mean of a set of probability measures under the optimal transport metric, and has numerous applications spanning machine learning, statistics, and computer graphics. In practice these input measures are empirical distributions built from sensitive datasets, motivating a differentially private (DP) treatment. We present, to our knowledge, the first algorithms for computing Wasserstein barycenters under differential privacy. Empirically, on synthetic data, MNIST, and large-scale U.S. population datasets, our methods produce high-quality private barycenters with strong accuracy-privacy tradeoffs.
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