Generalized Leverage Score for Scalable Assessment of Privacy Vulnerability
Valentin Dorseuil (DI-ENS), Jamal Atif (CMAP), Olivier Capp\'e (DI-ENS)

TL;DR
This paper introduces a scalable method to assess individual data point privacy vulnerability by generalizing leverage scores, linking them to membership inference attack risks without retraining models.
Contribution
It formalizes the connection between leverage scores and privacy risk, and extends this concept to deep learning for efficient vulnerability assessment.
Findings
Strong correlation between the proposed score and MIA success
Efficient computation of privacy vulnerability in deep models
Theoretical foundation linking influence and privacy risk
Abstract
Can the privacy vulnerability of individual data points be assessed without retraining models or explicitly simulating attacks? We answer affirmatively by showing that exposure to membership inference attack (MIA) is fundamentally governed by a data point's influence on the learned model. We formalize this in the linear setting by establishing a theoretical correspondence between individual MIA risk and the leverage score, identifying it as a principled metric for vulnerability. This characterization explains how data-dependent sensitivity translates into exposure, without the computational burden of training shadow models. Building on this, we propose a computationally efficient generalization of the leverage score for deep learning. Empirical evaluations confirm a strong correlation between the proposed score and MIA success, validating this metric as a practical surrogate for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Information and Cyber Security · Adversarial Robustness in Machine Learning
