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Low-Cost High-Power Membership Inference Attacks
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Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attacks (RMIA) with low computational overhead. We achieve this by a fine-grained modeling of the null hypothesis in our likelihood ratio tests, and effectively leveraging both reference models and reference population data samples. RMIA has superior test power compared with prior methods, throughout the TPR-FPR curve (even at extremely low FPR, as low as 0). Under computational constraints, where only a limited number of pre-trained reference models (as few as 1) are available, and also when we vary other elements of the attack (e.g., data distribution), our method performs exceptionally well, unlike prior attacks that approach random guessing. RMIA lays the groundwork for practical yet accurate data privacy risk assessment in machine learning.
Forward citations
Cited by 11 Pith papers
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Membership Inference Attacks for Unseen Classes
In a new 'unseen class' setting for membership inference, quantile regression attacks outperform shadow model attacks, which fall to the level of a global-threshold baseline.
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Vid-SME: Membership Inference Attacks against Large Video Understanding Models
Vid-SME computes Sharma-Mittal entropy differences between natural and reversed video frame sequences to infer training membership in video understanding LLMs, but its effectiveness is confounded by member/non-member ...
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Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
A model-agnostic audit detects synthetic data disclosures via feature-match and membership-inference tests that separate true from phantom leaks and give empirical differential-privacy lower bounds.
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Stealth by Conformity: Evading Robust Aggregation through Adaptive Poisoning
An adaptive federated-learning backdoor attack uses membership-inference feedback on the global model to keep malicious updates statistically similar to benign ones, evading nine robust aggregation defenses in two ima...
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Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
MIAs expose different members depending on attack method and random seed; the paper quantifies this with coverage/stability and shows ensembling attacks yields stronger, more reliable privacy checks.
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DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics
DeSIA infers sensitive attributes from limited fixed aggregate statistics by first checking whether a value is uniquely forced by the counts, then using a shadow-model classifier, outperforming reconstruction baseline...
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Black-Box Privacy Attacks on Shared Representations in Multitask Learning
A purely black-box attacker can infer whether a task was included in multitask training by measuring the variance or pairwise inner products of embedding vectors, with near-perfect success when tasks correspond to lab...
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Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Counting the iterations required to craft an adversarial example can reveal whether a sample was in the model's training set.
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Membership Inference Attacks with False Discovery Rate Control
A post-hoc wrapper, MIAFdR, converts any membership inference attack scores into conformal p-values and applies a Benjamini-Hochberg correction, guaranteeing that the expected proportion of non-members among flagged m...
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Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning
Per-sample gradient noise and exponential down-weighting of LIRA-identified vulnerable points reduce membership inference success on CIFAR-10 while keeping test accuracy near baseline.
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Synthetic Tabular Data: Methods, Attacks and Defenses
A review of tabular synthetic data generation, privacy attacks, and defenses, whose central message is that synthetic data alone does not guarantee privacy.
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