Near-Duplicate Families Break Exact-Record Membership Inference

arXiv:2609.33909 · cs.CR · Submitted 2026-09-27 · Read on arXiv

cs.CR

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• AUC — 61× in this paper · explained in 2 episodes
• Fidelity — 30× in this paper · explained in 2 episodes
• Calibration — 24× in this paper · explained in 7 episodes
• Provenance — 14× in this paper · explained in 3 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 5× in this paper
• Data Provenance — 4× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Abstention — 3× in this paper · explained in 1 episode
• Differential Privacy — 3× in this paper · explained in 4 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• SIGSAC Conference on Computer and Communications Security — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Area Under the ROC Curve — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Heteroscedasticity — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Local Invariance — 1× in this paper · explained in 2 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Unlocking High-Accuracy Differentially Private Image Classification through Scale
• On the Evidentiary Limits of Membership Inference for Copyright Auditing
• On the Discredibility of Membership Inference Attacks
• Qwen3 Technical Report
• Opacus: User-Friendly Differential Privacy Library in PyTorch

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