Data Aggregation without Secure Channel: How to Evaluate a Multivariate Polynomial Securely
This work addresses privacy-preserving data aggregation for distributed systems where secure channels are unavailable, offering a practical solution with incremental improvements in efficiency.
The paper tackles the problem of securely aggregating multivariate polynomial evaluations from multiple participants without requiring secure channels, ensuring data confidentiality against eavesdropping attacks while maintaining linear communication and computation complexity.
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.