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

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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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Dates and versions

hal-01384148 , version 1 (19-10-2016)

Identifiers

  • HAL Id : hal-01384148 , version 1

Cite

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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