@inproceedings{f45202c7693549adaf7f024aac4f3ecf,
title = "Differentially-Private Collaborative Online Personalized Mean Estimation",
abstract = "We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy. Two privacy mechanisms are proposed and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents' data. Numerical results show that for a considered scenario the proposed approach converges much faster than a fully local approach where agents do not share data, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.",
author = "Yauhen Yakimenka and Weng, {Chung Wei} and Lin, {Hsuan Yin} and Eirik Rosnes and J{\"o}rg Kliewer",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Symposium on Information Theory, ISIT 2023 ; Conference date: 25-06-2023 Through 30-06-2023",
year = "2023",
doi = "10.1109/ISIT54713.2023.10206796",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "737--742",
booktitle = "2023 IEEE International Symposium on Information Theory, ISIT 2023",
address = "United States",
}