arXiv AI June 2, 2026 By Rakshit Naidu

Fair Finetuning Mitigates Distribution Inference Attacks

arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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.

By Rakshit Naidu

nlp benchmarks safety

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.

By Jiankai Jin, Xiangzheng Zhang, Zhao Liu, Wenzhuo Xu, Dongdong Yang, Deyue Zhang, Quanchen Zou

efficiency multimodal safety

arXiv Machine Learning

Jul 15

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.

By Rasmus Torp, Shailen K. Smith, Adam Breuer

benchmarks safety

arXiv Machine Learning

Aug 10

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.

By Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, Jackson Walters

robotics benchmarks safety

arXiv Machine Learning

Jun 15

Private Prediction via PAC Privacy

arXiv:2601. 14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs.

By Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas

safety

arXiv AI

3d ago

Efficient Active Auditing of Multi-Group Fairness with Bias Probes

arXiv:2609.40034v1 Announce Type: cross Abstract: Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM)...

By Ayoub Ajarra, Debabrota Basu

safety

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Related stories

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

Robust Privacy: Inference-Stage Privacy through Certified Robustness

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

Private Prediction via PAC Privacy

Efficient Active Auditing of Multi-Group Fairness with Bias Probes