arXiv AI October 31, 2026 By Junwei Chen, Manjiang Yu, Pengpeng Qiao, Yang Cao

Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

The paper introduces a rank‑aware exponential mechanism that privatizes listwise preference data for large language model alignment under local differential privacy. By sampling a fixed‑size binary partition, the method concentrates noise near borderline items rather than uniformly across all items, thereby reducing sensitivity. Experiments demonstrate that this approach outperforms existing LDP baselines on downstream alignment tasks while maintaining the same privacy budget.

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arXiv AI

Jun 16

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.

By Zhikang Shen, Jianrong Lu, Haiyuan Wan, Jianhai Chen

llms fine-tuning benchmarks safety

arXiv Machine Learning

Sep 1

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

arXiv:2608.30141v1 Announce Type: cross Abstract: Preference optimization is widely used to align large language models with human preferences, but preference-data composition may also influence priv...

By Dishu Yang, Jingjing Liu, Jize Li

llms reinforcement-learning efficiency safety

arXiv Machine Learning

Oct 21

Guided Diffusion for Trustworthy Synthetic Text Generation

arXiv:2610.27259v1 Announce Type: cross Abstract: The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently...

By Qi Pang, Hanshen Xiao, Andre Manoel, Lipika Ramaswamy, Wenting Zheng, Virginia Smith, G. Edward Suh

llms rag diffusion fine-tuning safety

arXiv AI

Jun 10

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606. 10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples.

By Nicole Mitchell, Galen Andrew, Arun Ganesh, Brendan McMahan, Peter Kairouz

llms fine-tuning benchmarks safety

arXiv AI

Oct 9

Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning

Nullify is a training‑free method for unlearning sensitive information from large language models by steering activations during inference. It uses steering vectors to redirect privacy‑related activations away from memorized answers while enforcing a null‑space constraint that preserves activations for retained queries, thereby maintaining model utility. Experiments on TOFU and MUSE demonstrate that Nullify matches or outperforms existing baselines in forget quality while keeping utility nearly intact, all without any weight updates.

By Wei Zhai, Xiang Liu, Qiang Huang, Rui Qian, Lemao Liu, Ziwei Li, Ziqi Wang, Zhitao Huang, Dejing Dou

llms fine-tuning safety

arXiv Machine Learning

Jun 10

Alignment Defends LLMs from Property Inference Attacks

arXiv:2606. 10217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly fine-tuned on domain-specific datasets that may contain sensitive, dataset-level properties.

By Pengrun Huang, Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri

llms reinforcement-learning fine-tuning safety

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SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

Guided Diffusion for Trustworthy Synthetic Text Generation

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