A Decentralized and Robust Protocol for Private Averaging over Highly Distributed Data

Pierre Dellenbach 1 Jan Ramon 1 Aurélien Bellet 1
1 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
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https://hal.inria.fr/hal-01384148
Contributor : Aurélien Bellet <>
Submitted on : Wednesday, October 19, 2016 - 2:30:34 PM
Last modification on : Tuesday, September 10, 2019 - 11:32:02 AM

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  • HAL Id : hal-01384148, version 1

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Pierre Dellenbach, Jan Ramon, Aurélien Bellet. A Decentralized and Robust Protocol for Private Averaging over Highly Distributed Data. NIPS 2016 workshop on Private Multi-Party Machine Learning, Dec 2016, Barcelone, Spain. ⟨hal-01384148⟩

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