Private Model Personalization Revisited

• URL: http://arxiv.org/abs/2506.19220v1
• Date: Tue, 24 Jun 2025 00:57:17 GMT
• Title: Private Model Personalization Revisited
• Authors: Conor Snedeker, Xinyu Zhou, Raef Bassily,
• Abstract summary: We study model personalization under user-level differential privacy (DP) in the shared representation framework.<n>Our goal is to privately recover the shared embedding and the local low-dimensional representations with small excess risk.<n>We present an information-theoretic construction to privately learn the shared embedding and derive a margin-based accuracy guarantee.
• Score: 13.4143747448136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study model personalization under user-level differential privacy (DP) in the shared representation framework. In this problem, there are $n$ users whose data is statistically heterogeneous, and their optimal parameters share an unknown embedding $U^* \in\mathbb{R}^{d\times k}$ that maps the user parameters in $\mathbb{R}^d$ to low-dimensional representations in $\mathbb{R}^k$, where $k\ll d$. Our goal is to privately recover the shared embedding and the local low-dimensional representations with small excess risk in the federated setting. We propose a private, efficient federated learning algorithm to learn the shared embedding based on the FedRep algorithm in [CHM+21]. Unlike [CHM+21], our algorithm satisfies differential privacy, and our results hold for the case of noisy labels. In contrast to prior work on private model personalization [JRS+21], our utility guarantees hold under a larger class of users' distributions (sub-Gaussian instead of Gaussian distributions). Additionally, in natural parameter regimes, we improve the privacy error term in [JRS+21] by a factor of $\widetilde{O}(dk)$. Next, we consider the binary classification setting. We present an information-theoretic construction to privately learn the shared embedding and derive a margin-based accuracy guarantee that is independent of $d$. Our method utilizes the Johnson-Lindenstrauss transform to reduce the effective dimensions of the shared embedding and the users' data. This result shows that dimension-independent risk bounds are possible in this setting under a margin loss.

Related papers

• Smoothed Normalization for Efficient Distributed Private Optimization [54.197255548244705]
  Federated learning enables machine learning models with privacy of participants.<n>There is no differentially private distributed method for training, non-feedback problems.<n>We introduce a new distributed algorithm $alpha$-$sf NormEC$ with provable convergence guarantees.
  arXiv  Detail & Related papers  (2025-02-19T07:10:32Z)
• Differentially Private Random Feature Model [52.468511541184895]
  We produce a differentially private random feature model for privacy-preserving kernel machines.<n>We show that our method preserves privacy and derive a generalization error bound for the method.
  arXiv  Detail & Related papers  (2024-12-06T05:31:08Z)
• Optimized Tradeoffs for Private Prediction with Majority Ensembling [59.99331405291337]
  We introduce the Data-dependent Randomized Response Majority (DaRRM) algorithm.<n>DaRRM is parameterized by a data-dependent noise function $gamma$, and enables efficient utility optimization over the class of all private algorithms.<n>We show that DaRRM provably enjoys a privacy gain of a factor of 2 over common baselines, with fixed utility.
  arXiv  Detail & Related papers  (2024-11-27T00:48:48Z)
• Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning [47.042811490685324]
  Mitigating the risk of this information leakage, using state of the art differentially private algorithms, also does not come for free. In this paper, we consider a representation learning objective that various parties collaboratively refine on a federated model, with differential privacy guarantees. We observe a significant performance improvement over the prior work under the same small privacy budget.
  arXiv  Detail & Related papers  (2023-09-11T14:46:55Z)
• General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation [58.03500081540042]
  A classical approach to private mean estimation is to compute the true mean and add unbiased, but possibly correlated, Gaussian noise to it. We show that for every input dataset, an unbiased mean estimator satisfying concentrated differential privacy introduces approximately at least as much error.
  arXiv  Detail & Related papers  (2023-01-31T18:47:42Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• Pre-trained Perceptual Features Improve Differentially Private Image Generation [8.659595986100738]
  Training even moderately-sized generative models with differentially-private descent gradient (DP-SGD) is difficult. We advocate building off a good, relevant representation on an informative public dataset, then learning to model the private data with that representation. Our work introduces simple yet powerful foundations for reducing the gap between private and non-private deep generative models.
  arXiv  Detail & Related papers  (2022-05-25T16:46:01Z)
• Tight and Robust Private Mean Estimation with Few Users [16.22135057266913]
  We study high-dimensional mean estimation under user-level differential privacy. We design an $(eps,delta)$-differentially private mechanism using as few users as possible.
  arXiv  Detail & Related papers  (2021-10-22T16:02:21Z)
• Infinitely Divisible Noise in the Low Privacy Regime [9.39772079241093]
  Federated learning, in which training data is distributed among users and never shared, has emerged as a popular approach to privacy-preserving machine learning. We present the first divisible infinitely noise distribution for real-valued data that achieves $varepsilon$-differential privacy.
  arXiv  Detail & Related papers  (2021-10-13T08:16:43Z)
• Learning with User-Level Privacy [61.62978104304273]
  We analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samples, user-level DP protects a user's entire contribution. We derive an algorithm that privately answers a sequence of $K$ adaptively chosen queries with privacy cost proportional to $tau$, and apply it to solve the learning tasks we consider.
  arXiv  Detail & Related papers  (2021-02-23T18:25:13Z)
• Strongly universally consistent nonparametric regression and classification with privatised data [2.879036956042183]
  We revisit the classical problem of nonparametric regression, but impose local differential privacy constraints. We design a novel estimator of the regression function, which can be viewed as a privatised version of the well-studied partitioning regression estimator.
  arXiv  Detail & Related papers  (2020-10-31T09:00:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.