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.
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.
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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
Sep 11
SoK: Privacy Attacks on Machine Learning via Explainable AI
The paper surveys 25 studies that use explainable AI to compromise machine learning models, covering attacks such as model extraction, membership inference, and model inversion. It distinguishes between how explanations are obtained—through target releases, attacker-derived methods, secondary disclosure, privileged access, or global artifacts—and shows that explanations can lower extraction costs and reveal membership signals via statistics, recourse distance, and robustness. The authors compare threat models, signals, and defenses, concluding that no single explanation type is always unsafe and that protection must be tailored to the specific acquisition path and target asset.
arXiv Machine Learning
Jun 2
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.
arXiv Machine Learning
Sep 1
Revisiting the Provable-Auditable Privacy Gap of DP-SGD
arXiv:2608.28934v1 Announce Type: new Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In...
arXiv Machine Learning
Jun 2
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
arXiv:2409. 01062v4 Announce Type: replace Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models.
arXiv AI
Jun 15
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
arXiv:2606. 14210v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment.
arXiv Machine Learning
Jul 13
LDPKiT: Superimposing Remote Queries for Privacy-Preserving Distillation
arXiv:2405. 16361v4 Announce Type: replace Abstract: To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private.
arXiv Machine Learning
Jun 19
Predictability as a Fine-Grained Measure for Privacy
arXiv:2606. 20546v1 Announce Type: new Abstract: Differential privacy (DP) ensures rigorous individual-level privacy guarantees against even the most knowledgeable attackers, but its worst-case nature can impose a costly privacy-accuracy tradeoff.
Hugging Face Trending Papers
Jun 1
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
arXiv Machine Learning
Jun 9
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
arXiv:2606. 09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees.
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.