PAC learning halfspaces in non-interactive local differential privacy model with public unlabeled data

Jinyan Su, Jinhui Xu, Di Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexity, previous results studied a relaxed setting where the server has access to some additional public but unlabeled data. We continue in this direction. Specifically, we consider the problem under the standard setting instead of the large margin setting studied before. Under different mild assumptions on the underlying data distribution, we propose two approaches that are based on the Massart noise model and self-supervised learning and show that it is possible to achieve sample complexities that are only linear in the dimension and polynomial in other terms for both private and public data, which significantly improve the previous results. Our methods could also be used for other private PAC learning problems.

Original languageEnglish (US)
Article number103496
JournalJournal of Computer and System Sciences
Volume141
DOIs
StatePublished - May 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

Keywords

  • Differential privacy
  • PAC learning
  • Stochastic optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics

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