Making AI Forget You: Data Deletion in Machine Learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou

TL;DR
This paper addresses the challenge of efficiently deleting individual data points from trained machine learning models, proposing algorithms that significantly improve deletion efficiency while maintaining model quality.
Contribution
It introduces the first provably efficient data deletion algorithms for k-means clustering, reducing computational costs substantially compared to retraining.
Findings
Over 100X improvement in deletion efficiency across multiple datasets
Maintains comparable clustering quality to standard methods
Provides a framework for data deletion in ML models
Abstract
Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an example of this effort. In this paper we initiate a framework studying what to do when it is no longer permissible to deploy models derivative from specific user data. In particular, we formulate the problem of efficiently deleting individual data points from trained machine learning models. For many standard ML models, the only way to completely remove an individual's data is to retrain the whole model from scratch on the remaining data, which is often not computationally practical. We investigate algorithmic principles that enable efficient data deletion in ML. For the specific setting of k-means clustering, we propose two provably efficient deletion algorithms which achieve an average of over 100X…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Data Quality and Management · Explainable Artificial Intelligence (XAI)
