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Summary of Center-based Relaxed Learning Against Membership Inference Attacks, by Xingli Fang and Jung-eun Kim


Center-Based Relaxed Learning Against Membership Inference Attacks

by Xingli Fang, Jung-Eun Kim

First submitted to arxiv on: 26 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
In a quest to improve membership inference attack (MIA) defenses, researchers propose center-based relaxed learning (CRL), an architecture-agnostic training paradigm. CRL adapts to any classification model, sacrificing minimal loss of generalizability for privacy preservation. By maintaining consistency between member and non-member data, CRL achieves comparable performance on standard classification datasets without requiring additional model capacity or data costs.
Low GrooveSquid.com (original content) Low Difficulty Summary
Membership inference attacks are a significant threat to data privacy. Researchers propose a new approach called center-based relaxed learning (CRL) that improves defense against these attacks. CRL is an architecture-agnostic training method that adapts to any classification model, providing privacy preservation without sacrificing too much performance. This means you can still get accurate results while keeping your data private.

Keywords

» Artificial intelligence  » Classification  » Inference