Optimal Locally Private Nonparametric Classification with Public Data

• URL: http://arxiv.org/abs/2311.11369v3
• Date: Sun, 2 Jun 2024 10:46:32 GMT
• Title: Optimal Locally Private Nonparametric Classification with Public Data
• Authors: Yuheng Ma, Hanfang Yang,
• Abstract summary: We investigate the problem of public data assisted non-interactive Local Differentially Private (LDP) learning with a focus on non-parametric classification. Under the posterior drift assumption, we derive the mini-max optimal convergence rate with LDP constraint. We present a novel approach, the locally differentially private classification tree, which attains the mini-max optimal convergence rate.
• Score: 2.631955426232593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we investigate the problem of public data assisted non-interactive Local Differentially Private (LDP) learning with a focus on non-parametric classification. Under the posterior drift assumption, we for the first time derive the mini-max optimal convergence rate with LDP constraint. Then, we present a novel approach, the locally differentially private classification tree, which attains the mini-max optimal convergence rate. Furthermore, we design a data-driven pruning procedure that avoids parameter tuning and provides a fast converging estimator. Comprehensive experiments conducted on synthetic and real data sets show the superior performance of our proposed methods. Both our theoretical and experimental findings demonstrate the effectiveness of public data compared to private data, which leads to practical suggestions for prioritizing non-private data collection.

Related papers

• Minimax Optimal Two-Sample Testing under Local Differential Privacy [3.3317825075368908]
  We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) We introduce private permutation tests using practical privacy mechanisms such as Laplace, discrete Laplace, and Google's RAPPOR. We study continuous data via binning and study its uniform separation rates under LDP over H"older and Besov smoothness classes.
  arXiv  Detail & Related papers  (2024-11-13T22:44:25Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Locally Private Estimation with Public Features [2.9562742331218725]
  We study the study of locally differentially private (LDP) learning with public features. Under semi-feature LDP, we demonstrate that the mini-max convergence rate for non-parametric regression is significantly reduced. We propose an estimator that fully leverages the information contained in both public and private features.
  arXiv  Detail & Related papers  (2024-05-22T09:47:54Z)
• Partition-based differentially private synthetic data generation [0.5095097384893414]
  We present a partition-based approach that reduces errors and improves the quality of synthetic data, even with a limited privacy budget. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing.
  arXiv  Detail & Related papers  (2023-10-10T07:23:37Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Differentially Private Federated Clustering over Non-IID Data [59.611244450530315]
  clustering clusters (FedC) problem aims to accurately partition unlabeled data samples distributed over massive clients into finite clients under the orchestration of a server. We propose a novel FedC algorithm using differential privacy convergence technique, referred to as DP-Fed, in which partial participation and multiple clients are also considered. Various attributes of the proposed DP-Fed are obtained through theoretical analyses of privacy protection, especially for the case of non-identically and independently distributed (non-i.i.d.) data.
  arXiv  Detail & Related papers  (2023-01-03T05:38:43Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping [40.69950711262191]
  We consider differentially private convex optimization for heavy-tailed data with the guarantee of being differentially private (DP) We establish new convergence results and improved complexity bounds for the proposed algorithm called AClipped-dpSGD for constrained and unconstrained convex problems.
  arXiv  Detail & Related papers  (2022-06-27T01:39:15Z)
• Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization [4.554894288663752]
  We propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Unlike cross-validation, our approach avoids sacrificing data for a test set. We prove our estimator performs well in the small-data, largescale regime.
  arXiv  Detail & Related papers  (2021-07-26T19:00:51Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)
• User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization [77.43075255745389]
  Federated learning (FL) is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. From a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. We propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers.
  arXiv  Detail & Related papers  (2020-02-29T10:13:39Z)

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.