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Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness

Cheng-Long Wang, Qi Li, Zihang Xiang, Yinzhi Cao, Di Wang

TL;DR

This paper addresses the challenge of measuring sample-level unlearning completeness for approximate unlearning in deployed models. It introduces Lifecycle Unlearning Commitment Management (LUCM) and a non-membership inference–based metric, UnleScore, to monitor unlearning completeness efficiently in real time, without reliance on full retraining. Empirical results show UnleScore correlates strongly with actual unlearning degree, detects under- and over-unlearning, and is about 10× more computationally efficient than traditional MI-based methods. Benchmarking across seven approximate unlearning baselines on five datasets reveals resilience and equity risks unique to lifecycle unlearning, underscoring the need for LUCM in managing ongoing data deletion commitments; the authors also plan to open-source a new benchmark. Overall, LUCM provides a practical, scalable framework for monitoring and improving unlearning commitments in large, real-world systems.

Abstract

By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a less resource-demanding alternative to the more laborious exact unlearning methods. Yet, the unlearning completeness of target samples-even when the approximate algorithms are executed faithfully without external threats-remains largely unexamined, raising questions about those approximate algorithms' ability to fulfill their commitment of unlearning during the lifecycle. In this paper, we introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning and outline its primary challenges. We propose an efficient metric designed to assess the sample-level unlearning completeness. Our empirical results demonstrate its superiority over membership inference techniques in two key areas: the strong correlation of its measurements with unlearning completeness across various unlearning tasks, and its computational efficiency, making it suitable for real-time applications. Additionally, we show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle, including both under-unlearning and over-unlearning. We apply this metric to evaluate the unlearning commitments of current approximate algorithms. Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups. These insights emphasize the crucial importance of LUCM throughout the unlearning lifecycle. We will soon open-source our newly developed benchmark.

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness

TL;DR

This paper addresses the challenge of measuring sample-level unlearning completeness for approximate unlearning in deployed models. It introduces Lifecycle Unlearning Commitment Management (LUCM) and a non-membership inference–based metric, UnleScore, to monitor unlearning completeness efficiently in real time, without reliance on full retraining. Empirical results show UnleScore correlates strongly with actual unlearning degree, detects under- and over-unlearning, and is about 10× more computationally efficient than traditional MI-based methods. Benchmarking across seven approximate unlearning baselines on five datasets reveals resilience and equity risks unique to lifecycle unlearning, underscoring the need for LUCM in managing ongoing data deletion commitments; the authors also plan to open-source a new benchmark. Overall, LUCM provides a practical, scalable framework for monitoring and improving unlearning commitments in large, real-world systems.

Abstract

By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a less resource-demanding alternative to the more laborious exact unlearning methods. Yet, the unlearning completeness of target samples-even when the approximate algorithms are executed faithfully without external threats-remains largely unexamined, raising questions about those approximate algorithms' ability to fulfill their commitment of unlearning during the lifecycle. In this paper, we introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning and outline its primary challenges. We propose an efficient metric designed to assess the sample-level unlearning completeness. Our empirical results demonstrate its superiority over membership inference techniques in two key areas: the strong correlation of its measurements with unlearning completeness across various unlearning tasks, and its computational efficiency, making it suitable for real-time applications. Additionally, we show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle, including both under-unlearning and over-unlearning. We apply this metric to evaluate the unlearning commitments of current approximate algorithms. Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups. These insights emphasize the crucial importance of LUCM throughout the unlearning lifecycle. We will soon open-source our newly developed benchmark.
Paper Structure (39 sections, 1 theorem, 11 equations, 32 figures, 4 tables)

This paper contains 39 sections, 1 theorem, 11 equations, 32 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Machine Unlearning
  4. Failure of Existing Unlearning Measurements
  5. Methodology
  6. Approximate Unlearning System
  7. Formalizing Unlearning Completeness
  8. Non-membership Inference in LUCM
  9. Experimental Evaluation Setup
  10. Metrics Baselines
  11. Unlearning Metric Validation
  12. Assessing Metrics' Measurement Utility in Exact Unlearning Tasks
  13. Metric Threshold for Sufficient Unlearning
  14. Correlation Between UnleScore and the Degree of Unlearning Implementation
  15. Computation Efficiency
  16. ...and 24 more sections

Key Result

Theorem 1

The conditional probability $P(B|A)$ equals the marginal probability $P(B)$ when event $A$—the inclusion of sample $x$ in the original model $\theta_{\text{ori}}$'s training set—is confirmed ($P(A)=1$), simplifying the assessment of unlearning's efficacy.

Figures (32)

  • Figure 1: Confusion Matrix Shifts from Membership to Non-Membership
  • Figure 2: Membership Prefer vs. Non-Membership Prefer
  • Figure 3: MIA for Unlearning vs. Unlearning Completeness Measurement
  • Figure 4: Statistical results (NMI_TPR@FPR=0.01‰) of metric utility on 3 types of exact unlearning.
  • Figure 5: Score Distributions of Unlearning Metrics with Different Unlearning Tasks on CIFAR10. See Appendix \ref{['sec:subsec:metricthreshold_add']} for results on other datasets.
  • ...and 27 more figures

Theorems & Definitions (2)

  • Theorem 1: Conditional Probability of Unlearning Auditing
  • proof