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Peter Kairouz
Researcher at Google
Publications - 121
Citations - 6780
Peter Kairouz is an academic researcher from Google. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 23, co-authored 101 publications receiving 3578 citations. Previous affiliations of Peter Kairouz include University of Illinois at Urbana–Champaign & Qualcomm.
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Proceedings ArticleDOI
Asynchronous and noncoherent neighbor discovery for the IoT using sparse-graph codes
TL;DR: A novel asynchronous group testing scheme is formed and it is shown that the proposed scheme is able to detect the set of K active neighbors1 among a network of n nodes with codeword length and decoding complexity of Θ(K log (K) log (n)).
Journal ArticleDOI
Challenges towards the Next Frontier in Privacy
Rachel Cummings,Damien Desfontaines,D. F. Evans,Roxana Geambasu,Matthew Jagielski,Yangsibo Huang,Peter Kairouz,Gautam Kamath,Se-Heum Oh,Olga Ohrimenko,Nicolas Papernot,Ryan Rogers,Milan Shen,Shuang Song,Weijie J. Su,Andreas Terzis,Abhradeep Guha Thakurta,Sergei Vassilvitskii,Li Xiong,Sergey Yekhanin,Da Yu,Huanyu Zhang,Wanrong Zhang +22 more
TL;DR: Differential privacy (DP): Challenges towards the next frontier workshop as discussed by the authors was held in 2019 with experts from industry, academia, and the public sector to seek answers to broad questions pertaining to privacy and its implications in the design of industry-grade systems.
Generative Adversarial Models for Learning Private and Fair Representations
TL;DR: Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations, is presented and it is shown that for appropriately chosen adversarial loss functions, GAPF provides privacy guarantees against strong Information Theory adversaries and enforces demographic parity.
Journal ArticleDOI
Federated learning and privacy
TL;DR: Building privacy-preserving systems for machine learning and data science on decentralized data on centralized data is a good start.
Proceedings Article
Context Aware Local Differential Privacy
TL;DR: In this paper, context-aware local differential privacy (LDP) is proposed to incorporate the application's context into the privacy definition, which can be used for geolocation and web search applications.