We consider a sequential setting in which a single dataset of individuals is used to perform adaptively-chosen analyses, while ensuring that the differential privacy loss of each participant does not exceed a pre-specified privacy budget. The standard approach to this problem relies on bounding a worst-case estimate of the privacy loss over all individuals and all possible values of their data, for every single analysis. Yet, in many scenarios this approach is overly conservative, especially for ``typical'' data points which incur little privacy loss by participation in most of the analyses. In this work, we give a method for tighter privacy loss accounting based on the value of a personalized privacy loss estimate for each individual in each analysis. The accounting method relies on a new composition theorem for R\'enyi differential privacy, which allows adaptively-chosen privacy parameters. We apply our results to the analysis of noisy gradient descent and show how existing algorithms can be generalized to incorporate individual privacy accounting and thus achieve a better privacy-utility tradeoff.

研究了如何在单个数据集上执行自适应分析，确保每个参与者的差分隐私损失不超过预先指定的预算，提出了一种基于个性化隐私损失估计的更紧密的隐私损失计算方法，并应用于噪声梯度下降分析中。

基于Renyi过滤器的个人隐私账户管理