A Decentralized and Robust Protocol for Private Averaging over Highly Distributed Data
| Autor: | Dellenbach, Pierre, Ramon, Jan, Bellet, Aurélien |
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| Přispěvatelé: | Machine Learning in Information Networks (MAGNET), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), ANR-16-CE23-0016,PAMELA,Apprentissage automatique décentralisé et personnalisé sous contraintes(2016) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | NIPS 2016 workshop on Private Multi-Party Machine Learning NIPS 2016 workshop on Private Multi-Party Machine Learning, Dec 2016, Barcelone, Spain |
| Popis: | International audience; We propose a decentralized protocol for a large set of users to privately compute averages over their joint data, which can later be used to learn more complex models. Our protocol can find a solution of arbitrary accuracy, does not rely on a trusted third party and preserves the privacy of users throughout the execution in both the honest-but-curious and malicious adversary models. Furthermore, we design a verification procedure which offers protection against malicious users joining the service with the goal of manipulating the outcome of the algorithm. |
| Databáze: | OpenAIRE |
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