Machine Unlearning as Private Retroactive Algorithms

2026-09-04

AI Summary

本文针对机器遗忘的隐私问题，提出了一种结合历史修正与差分隐私的私有回顾性算法，并展示了在统计、聚类和直方图上的应用及不可能性结果。

Abstract

Machine unlearning typically aims to emulate retraining from scratch: upon a deletion request, the unlearning algorithm should produce an outcome that would have been obtained had the deleted point never been included. Recent work has shown that this emulation requirement carries no meaningful privacy semantics against an adversary who observes a sequence of releases. Machine unlearning is thus not a privacy question per se, but rather a data maintenance question, which is precisely the subject of retroactive algorithms. These are algorithms supporting modifications of past operations, guaranteeing that all subsequent answers reflect the revised history as if it had always been in force. We put forward a definition of private retroactive algorithms, combining the retroactivity requirement with differential privacy under continual observation. We present constructions achieving both privacy and retroactivity at no asymptotic cost over privacy alone for linear statistics, clustering, and histograms, alongside impossibility results.

Problem

• machine unlearning
• retroactive algorithms
• differential privacy
• data maintenance

Innovation

• private retroactive algorithms
• differential privacy
• continual observation
• linear statistics
• clustering