arXiv Machine Learning
Jul 23
Membership Inference Attacks for Unseen Classes
arXiv:2506. 06488v3 Announce Type: replace Abstract: A key tool in developing safe AI models is \emph{data auditing}, i.
Exponential-Family Membership Inference: From LiRA and RMIA to BaVarIA
arXiv:2603. 11799v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) are becoming standard tools for auditing the privacy of machine learning models.
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arXiv Machine Learning
Aug 11
Score-based Membership Inference on Diffusion Models
arXiv:2509. 25003v3 Announce Type: replace Abstract: Membership inference attacks (MIAs) against Diffusion Models (DMs) raise pressing privacy concerns by revealing whether a sample was part of the training set.
arXiv Machine Learning
Jun 17
CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
arXiv:2606. 17464v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties.
arXiv Machine Learning
Jun 24
LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
arXiv:2602. 18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during training.
arXiv AI
Jul 21
Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning
arXiv:2607. 16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems.
arXiv Machine Learning
Aug 4
Private Generative Bootstrap via Blocking
arXiv:2608. 02480v1 Announce Type: cross Abstract: With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers.
arXiv Machine Learning
Jun 3
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
arXiv:2605. 11170v2 Announce Type: replace Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large-scale deletion requests.
arXiv AI
Jun 11
Robust Privacy: Inference-Stage Privacy through Certified Robustness
arXiv:2601. 17360v2 Announce Type: replace-cross Abstract: An adversary observing a model's released prediction can infer sensitive attributes of the queried input, or even reconstruct representatives of the model's training data.
arXiv Machine Learning
Aug 11
Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators
arXiv:2603. 10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography.
arXiv AI
Jul 14
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
arXiv:2506. 20893v5 Announce Type: replace-cross Abstract: In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause information leakage about the forgotten class.
arXiv Machine Learning
Aug 4
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
arXiv:2410. 06814v2 Announce Type: replace Abstract: Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model.
arXiv Machine Learning
Jun 26
Dataset Usage Inference without Shadow Models or Held-out Data
arXiv:2606. 26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model?