Article

Prio: private, robust, and scalable computation of aggregate statistics

Authors:

Henry Corrigan-Gibbs and

Dan BonehAuthors Info & Claims

NSDI'17: Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation

March 2017

Pages 259 - 282

Published: 27 March 2017 Publication History

Abstract

This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.

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NSDI'17: Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation

March 2017

749 pages

ISBN:9781931971379

Program Chairs:
Aditya Akella
University of Wisconsin-Madison
,
Jon Howell
Google

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Dauterman EFeng ELuo EPopa RStoica ILu SHowell J(2020)DORYProceedings of the 14th USENIX Conference on Operating Systems Design and Implementation10.5555/3488766.3488828(1101-1119)Online publication date: 4-Nov-2020
https://dl.acm.org/doi/10.5555/3488766.3488828
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