Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
Authors: H. Corrigan-Gibbs and D. Boneh
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.
Reference:
In proceedings of NSDI 2017, pp. 76-81.
Full paper: pdf
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