3 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
Tatsuki Koga, Ruihan Wu, Zhiyuan Zhang +1
With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outs…
Influence-based Attributions can be Manipulated
Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri
Influence Functions are a standard tool for attributing predictions to training data in a principled manner and are widely used in applications such as data valuation and fairness.…
Evaluating Deep Unlearning in Large Language Models
Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov +1
Machine unlearning has emerged as an important component in developing safe and trustworthy models. Prior work on fact unlearning in LLMs has mostly focused on removing a specified…
Better Membership Inference Privacy Measurement through Discrepancy
Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri
Membership Inference Attacks have emerged as a dominant method for empirically measuring privacy leakage from machine learning models. Here, privacy is measured by the {\em{advanta…