368 citations · 1.5k across the 66 of their papers we have counts for
24 papers · 1 filter
CLIOPATRA: Extracting Private Information from LLM Insights
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Peter Kairouz
The widespread adoption of AI assistants has encouraged the development of privacy-aware platforms designed to extract insights from real-world usage. Their privacy protections pri…
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
Toward provably private analytics and insights into GenAI use
Albert Cheu, Artem Lagzdin, Brett McLarnon +8
Large-scale systems that compute analytics over a fleet of devices must achieve high privacy and security standards while also meeting data quality, usability, and resource efficie…
Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data
Christopher Bian, Albert Cheu, Stanislav Chiknavaryan +12
This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Ma…
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…
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…
Releasing Large-Scale Human Mobility Histograms with Differential Privacy
Christopher Bian, Albert Cheu, Yannis Guzman +4
Environmental Insights Explorer (EIE) is a Google product that reports aggregate statistics about human mobility, including various methods of transit used by people across roughly…
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…
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…
Confidential Federated Computations
Hubert Eichner, Daniel Ramage, Kallista Bonawitz +11
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limita…
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,…
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea +3
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of f…
Private Federated Learning in Gboard
Yuanbo Zhang, Daniel Ramage, Zheng Xu +3
This white paper describes recent advances in Gboard(Google Keyboard)'s use of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, and secure aggregation tech…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
Differentially Private Stream Processing at Scale
Bing Zhang, Vadym Doroshenko, Peter Kairouz +6
We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-S…
The Poisson binomial mechanism for secure and private federated learning
Wei-Ning Chen, Ayfer Özgür, Peter Kairouz
We introduce the Poisson Binomial mechanism (PBM), a discrete differential privacy mechanism for distributed mean estimation (DME) with applications to federated learning and analy…
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation
Jiang Zhang, Lillian Clark, Matthew Clark +2
Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network p…
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem +4
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data bec…
Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah, Wei-Ning Chen, Johannes Balle +2
Compressing the output of ε-locally differentially private (LDP) randomizers naively leads to suboptimal utility. In this work, we demonstrate the benefits of using schemes that jo…
On the Renyi Differential Privacy of the Shuffle Model
Antonious M. Girgis, Deepesh Data, Suhas Diggavi +2
The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model. In the shuffle model,…
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz, Brendan McMahan, Shuang Song +3
We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), r…
Federated Heavy Hitters Discovery with Differential Privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan +2
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if no…
Hiding the Rumor Source
Giulia Fanti, Peter Kairouz, Sewoong Oh +2
Anonymous social media platforms like Secret, Yik Yak, and Whisper have emerged as important tools for sharing ideas without the fear of judgment. Such anonymous platforms are also…
Differentially Private Multi-party Computation: Optimality of Non-Interactive Randomized Response
Peter Kairouz, Sewoong Oh, Pramod Viswanath
We study the problem of interactive function computation by multiple parties possessing a single bit each in a differential privacy setting (i.e., there remains an uncertainty in a…