5 papers
Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning
Marshall Ball, James Bell-Clark, Adria Gascon +3
Recent advances in differentially private federated learning (DPFL) algorithms have found that using correlated noise across the rounds of federated learning (DP-FTRL) yields prova…
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…
Randomization Techniques to Mitigate the Risk of Copyright Infringement
Wei-Ning Chen, Peter Kairouz, Sewoong Oh +1
In this paper, we investigate potential randomization approaches that can complement current practices of input-based methods (such as licensing data and prompt filtering) and outp…
Privacy-Preserving Instructions for Aligning Large Language Models
Da Yu, Peter Kairouz, Sewoong Oh +1
Service providers of large language model (LLM) applications collect user instructions in the wild and use them in further aligning LLMs with users' intentions. These instructions,…
Improved Communication-Privacy Trade-offs in Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen, Berivan Isik, Peter Kairouz +3
We study mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simu…