Individual Privacy Accounting via a Renyi Filter
By Vitaly Feldman, Tijana Zrnic. 2020
119 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- James Y. Zou
- Antonio A. Ginart
- Chuan Guo
- L. V. D. van der Maaten
- Percy Liang
- Quoc V. Le
- Sergey Levine
- Mohit Bansal
Citations in the corpus
- Submix: Practical Private Prediction for Large-Scale Language Models (2022, methodology): “We will use the adaptive sequential composition theorem for RDP filters (Feldman & Zrnic, 2021, Theorem 4.3).”