Towards Reliable Empirical Machine Unlearning Evaluation: A Cryptographic Game Perspective

2024-04-17

AI Summary

Existing machine unlearning algorithms lack theoretically grounded evaluation frameworks for verifying whether specific training samples have been provably erased—critical for compliance with data protection regulations such as the GDPR. Method: This paper introduces the first evaluation paradigm with provable security guarantees by formalizing unlearning assessment as a cryptographic game between an unlearner and a membership inference attacker. We propose a game-theoretic unlearning metric and develop an efficient, computationally tractable statistical test approximation that integrates cryptographic game modeling, membership inference attacks, and hypothesis testing. Contribution/Results: Extensive experiments across multiple datasets and unlearning algorithms demonstrate that our metric significantly outperforms existing evaluation methods in robustness, discriminability, and reliability—offering the first principled, security-aware framework for quantifying unlearning efficacy.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Adversarial Learning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics lacking theoretical understanding and reliability. Specifically, by modeling the proposed evaluation process as a emph{cryptographic game} between unlearning algorithms and MIA adversaries, the naturally-induced evaluation metric measures the data removal efficacy of unlearning algorithms and enjoys provable guarantees that existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient approximation of the induced evaluation metric and demonstrate its effectiveness through both theoretical analysis and empirical experiments. Overall, this work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.

Problem

• Evaluates machine unlearning algorithm efficacy
• Addresses reliability of existing evaluation metrics
• Proposes cryptographic game-based evaluation method

Innovation

• Cryptographic game modeling
• Provable metric guarantees
• Efficient approximation technique