SplitX: high-performance private analytics

By Ruichuan Chen, Istemi Ekin Akkus, Paul Francis. 2013

63 citations recorded by the source corpus.

There is a growing body of research on mechanisms for preserving online user privacy while still allowing aggregate queries over private user data. A common approach is to store user data at users' devices, and to query the data in such a way that a differentially private noisy result is produced without exposing individual user data to any system component. A particular challenge is to design a system that scales well while limiting how much the malicious users can distort the result. This paper presents SplitX, a high-performance analytics system for making differentially private queries over distributed user data. SplitX is typically two to three orders of magnitude more efficient in bandwidth, and from three to five orders of magnitude more efficient in computation than previous comparable systems, while operating under a similar trust model. SplitX accomplishes this performance by replacing public-key operations with exclusive-or operations. This paper presents the design of SplitX, analyzes its security and performance, and describes its implementation and deployment across 416 users.

Subfields: Artificial Intelligence

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Citations in the corpus

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”
Efficient Private Statistics with Succinct Sketches (2015, background): “In [18], the authors propose a scheme based on a similar trust model as [19] but with an enhanced scalability by using simple exclusive-or (XOR) operations rather than public key operations.”
Distributed Differential Privacy and Applications (2015, background): “SplitX, by Chen, Akkus, and Francis (2013), is an evolution of the PDDP design, and focuses on the same setting of analysts and distributed horizontally-partitioned clients.”
Privacy and Access Control for Outsourced Personal Records (2015, background): “Integrity : clients can only write entries they hold write per-missions on.”
Differentially private data aggregation with optimal utility (2014, background): “Several specialized ad-hoc cryptographic protocols have been proposed recently to solve the problem of data aggregation in a distributed, privacypreserving manner, which has enabled the enforcement of DDP in some challenging scenarios, such as smart metering [13,24] and web analytics [7, 20,21].”