Lightweight Self-Bootstrapping Multiparty Computations of Time-Series Data with Custom Collusion Tolerance
This work addresses performance optimization in secure multiparty computations for time-series data, but it is incremental as it builds on existing protocols.
The paper compares two multiparty computation protocols for private summation, introducing a technique to reduce computational load at the expense of collusion tolerance, and provides experimental performance evaluations with security proofs.
In this work we compare two recent multiparty computation (MPC) protocols for private summation in terms of performance. Both protocols allow multiple rounds of aggregation from the same set of public keys generated by parties in an initial stage. We instantiate the protocols with a fast elliptic curve and provide an experimental comparison of their performance for different phases of the protocol. Furthermore, we introduce a technique that allows the computational load of both protocols to be reduced at the expense of protection against collusion tolerance. We prove that both protocols remain secure with this technique, and evaluate its impact on collusion tolerance and the number of rounds supported.