2 citations · 3 across the 5 of their papers we have counts for
5 papers
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
Ilia Shumailov, Daniel Ramage, Sarah Meiklejohn +4
We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data.…
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…
CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data
Zhao Cheng, Diane Wan, Matthew Abueg +6
Advances in generative AI point towards a new era of personalized applications that perform diverse tasks on behalf of users. While general AI assistants have yet to fully emerge,…
Operationalizing Contextual Integrity in Privacy-Conscious Assistants
Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi +10
Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramati…
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
Eugene Bagdasarian, Ren Yi, Sahra Ghalebikesabi +5
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns. While these agents excel at understandi…