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Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics

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arxiv 2009.05669 v1 pith:GW33J535 submitted 2020-09-11 cs.LG cs.CRstat.ML

Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics

Jason W. Bentley , Daniel Gibney , Gary Hoppenworth , Sumit Kumar Jha This is my paper
classification cs.LG cs.CRstat.ML
keywords attackmodelgeneralizationinferencemembershipmetricsaccessaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We demonstrate how a target model's generalization gap leads directly to an effective deterministic black box membership inference attack (MIA). This provides an upper bound on how secure a model can be to MIA based on a simple metric. Moreover, this attack is shown to be optimal in the expected sense given access to only certain likely obtainable metrics regarding the network's training and performance. Experimentally, this attack is shown to be comparable in accuracy to state-of-art MIAs in many cases.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Unified Perspective on Adversarial Membership Manipulation in Vision Models

    cs.CV 2026-04 conditional novelty 8.0

    Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.

  2. Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation

    cs.LG 2025-09 conditional novelty 6.0

    A bilevel optimization framework with curvature-guided geodesic perturbation reduces membership inference attack success while preserving downstream classification accuracy and sample quality.