High-Throughput Three-Party DPFs with Applications to ORAM and Digital Currencies

Abstract

specific and general secure computation. While two-party DPF constructions are readily available for those applications with satisfiable performance, the three-party ones are left behind in both security and efficiency. In this paper we close this gap and propose the first three-party DPF construction that matches the state-of-the-art two-party DPF on all metrics. Namely, it is secure against a malicious adversary corrupting both the dealer and one out of the three evaluators, its function's shares are of the same size and evaluation takes the same time as in the best two-party DPF. Compared to the state-of-the-art three-party DPF, our construction enjoys 40-120× smaller function's share size and shorter evaluation time, for function domains of 216 -240, respectively. Apart from DPFs as a stand-alone tool, our construction finds immediate applications to private information retrieval (PIR), writing (PIW) and oblivious RAM (ORAM). To further showcase its applicability, we design and implement an ORAM with access policy, an extension to ORAMs where a policy is being checked before accessing the underlying database. The policy we plug-in is the one suitable for account-based digital currencies, and in particular to central bank digital currencies (CBDCs). Our protocol offers the first design and implementation of a large scale privacy-preserving account-based digital currency. While previous works supported anonymity sets of 64-256 clients and less than 10 transactions per second (tps), our protocol supports anonymity sets in the millions, performing {500,200,58} tps for anonymity sets of {216, 218, 220}, respectively. Toward that application, we introduce a new primitive called updatable DPF, which enables a direct computation of a dot product between a DPF and a vector; we believe that updatable DPF and the new dot-product protocol will find interest in other applications.