P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users

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Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “Private data-collection systems [30, 48, 53, 56, 79, 86, 92] that use secret-sharing based methods to compute sums over private user data typically (a) provide no robustness guarantees in the face of malicious clients, (b) use expensive NIZKs to prevent client misbehavior, or (c) fail to defend priv”
Non-tracking web analytics (2012, background): “utilize two honest-but-curious (HbC) servers to add noise, and guarantee accuracy via relatively efficient, but still costly zero-knowledge proofs [33].”
Adversarial machine learning (2019, background): “An orthogonal body of work exists, in which secure multiparty computation is applied to machine learning [18, 29].”
SplitX: high-performance private analytics (2013, background): “P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users.”
Distributed Differential Privacy and Applications (2015, background): ““Peers for Privacy” or P4P, by Duan, Canny, and Zhan (2010), is a system designed to execute machine learning workloads in a distributed private fashion.”
Differentially private iterative synchronous consensus (2012, background): “In [7] a framework for securely computing general types of aggregates is presented.”