Efficient Private Statistics with Succinct Sketches

TL;DR

This paper introduces practical cryptographic techniques combined with succinct data sketches to enable privacy-preserving collection of large-scale statistics with reduced communication costs and bounded error.

Contribution

It presents a novel integration of cryptographic protocols with Count-Min and Count Sketch data structures for efficient, privacy-preserving data aggregation.

Findings

Reduced communication complexity from linear to logarithmic in input size.

Achieved bounded error that maintains statistical accuracy.

Demonstrated real-world applications like media recommendations and location prediction.

Abstract

Large-scale collection of contextual information is often essential in order to gather statistics, train machine learning models, and extract knowledge from data. The ability to do so in a {\em privacy-preserving} way -- i.e., without collecting fine-grained user data -- enables a number of additional computational scenarios that would be hard, or outright impossible, to realize without strong privacy guarantees. In this paper, we present the design and implementation of practical techniques for privately gathering statistics from large data streams. We build on efficient cryptographic protocols for private aggregation and on data structures for succinct data representation, namely, Count-Min Sketch and Count Sketch. These allow us to reduce the communication and computation complexity incurred by each data source (e.g., end-users) from linear to logarithmic in the size of their input, while introducing a parametrized upper-bounded error that does not compromise the quality of the statistics. We then show how to use our techniques, efficiently, to instantiate real-world privacy-friendly systems, supporting recommendations for media streaming services, prediction of user locations, and computation of median statistics for Tor hidden services.