Scalable, On-Demand Secure Multiparty Computation for Privacy-Aware Blockchains

Abstract

In private, permissioned blockchains, organizations desire to transact with one another in a privacy-aware manner. For instance, when Alice sends X crypto-tokens to Bob at time t, it is desirable for Alice and Bob to perform double-spending check without revealing each other’s token balance. This also illustrates the fact that some input data from individual party is needed for secure computation in order to produce result data forming transaction details. In this paper, we consider secure computations in a blockchain involving multiple parties: Whenever a party has sensitive data to be computed with other parties, there is a need to exercise secure multiparty data sharing and computation (SMPC) among the parties (where parties may be malicious) to yield the result. Conventional SMPC is not scalable for a blockchain that has thousands of parties (blockchain nodes), and where secure computations may not always involve all blockchain nodes all the time, and the practical need for secure computation may range from sporadic to frequent. In this paper, we address these issues by designing a scheme that allows SMPC to be conveniently launched on-demand by any number of k-clique subsets of blockchain nodes. We show that our scheme is secure against any input data leakage and output leakage before, during, and after SMPC.