论文标题
通过改组Rényi和近似差异隐私来扩大更强的隐私放大
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
论文作者
论文摘要
作为标准本地模型和中央模型之间的中间信任模型，差异隐私的洗牌模型已引起了人们的极大兴趣[EFMRTT19； CSUZZ19]。该模型的一个关键结果是，随机洗牌本地随机数据放大了差异隐私保证。这种放大意味着对数据匿名贡献的系统提供了更大的隐私保证[BEMMRLLKTS17]。 在这项工作中，我们通过在理论上和数字上通过改组结果来改善最新隐私放大的状态。我们的第一个贡献是对LDP Randomizer的改组输出的Rényi差异隐私参数的首次渐近最佳分析。我们的第二个贡献是通过改组对隐私放大的新分析。该分析改进了[FMT20]的技术，并导致所有参数设置中的数值范围更紧密。
The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17]. In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the Rényi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of [FMT20] and leads to tighter numerical bounds in all parameter settings.
