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 |
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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