Data Aggregation without Secure Channel: How to Evaluate a Multivariate Polynomial Securely

TL;DR

This paper presents a method for secure data aggregation over insecure channels, enabling algebraic computations like sums and products without revealing individual data, while maintaining low communication and computation costs.

Contribution

It introduces a novel approach to secure data aggregation that does not rely on secure channels and achieves linear complexity in communication and computation.

Findings

Guarantees data confidentiality over insecure channels

Limits communication and computation complexity to linear

Supports algebraic statistics like summation and product

Abstract

Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either assume to have a secure channel or suffer from high complexity. Here we consider how an external aggregator or multiple parties learn some algebraic statistics (e.g., summation, product) over participants' data while any individual's input data is kept secret to others (the aggregator and other participants). We assume channels in our construction are insecure. That is, all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We successfully guarantee data confidentiality under this weak assumption while limiting both the communication and computation complexity to at most linear.