Differentially Private Model PersonalizationDownload PDF

21 May 2021, 20:47 (modified: 22 Jan 2022, 16:10)NeurIPS 2021 SpotlightReaders: Everyone
Keywords: Differential Privacy, Model Personalization, Alternating Minimization, Learning Theory
TL;DR: In this paper, we formulate a model for reasoning rigorously about the loss to privacy incurred by sharing information for model personalization.
Abstract: We study personalization of supervised learning with user-level differential privacy. Consider a setting with many users, each of whom has a training data set drawn from their own distribution $P_i$. Assuming some shared structure among the problems $P_i$, can users collectively learn the shared structure---and solve their tasks better than they could individually---while preserving the privacy of their data? We formulate this question using joint, user-level differential privacy---that is, we control what is leaked about each user's entire data set. We provide algorithms that exploit popular non-private approaches in this domain like the Almost-No-Inner-Loop (ANIL) method, and give strong user-level privacy guarantees for our general approach. When the problems $P_i$ are linear regression problems with each user's regression vector lying in a common, unknown low-dimensional subspace, we show that our efficient algorithms satisfy nearly optimal estimation error guarantees. We also establish a general, information-theoretic upper bound via an exponential mechanism-based algorithm.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
12 Replies

Loading