论文标题
跨两个各方的私人独立测试
Private independence testing across two parties
论文作者
论文摘要
我们介绍了$π$ -Test，这是一种用于测试跨多方分布的数据之间统计独立性的隐私保护算法。我们的算法依赖于私人估计数据集之间的距离相关性，这是Székely等人中引入的独立性的定量度量。 [2007]。我们在差异私有测试的实用性上建立了加性和乘法误差界，我们相信在涉及敏感数据的各种分布式假设测试设置中，我们会发现应用程序。
We introduce $π$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. Our algorithm relies on privately estimating the distance correlation between datasets, a quantitative measure of independence introduced in Székely et al. [2007]. We establish both additive and multiplicative error bounds on the utility of our differentially private test, which we believe will find applications in a variety of distributed hypothesis testing settings involving sensitive data.
