In a seminal work, Ishai et al. (FOCS-2006) studied the viability of designing unconditionally secure protocols for key agreement and secure multi-party computation (MPC) using an anonymous bulletin board (ABB) as a building block. While their results establish the feasibility of key agreement and honest-majority MPC in the ABB model, the optimality of protocols with respect to their round and communication complexity is not studied. This paper enriches this study of unconditional security in the ABB model in multiple ways. •We present a key agreement protocol with a novel combinatorial insight to offer a 200% throughput over the (FOCS-2006) study; i.e., using the same number of messages, we can (almost) double the bit-length of the agreed key. We also prove the near optimality of our approach. •We offer unconditionally secure protocols for the (random) string oblivious transfer functionalities. We present a 1-round chosen message random string oblivious transfer and show how to extend it to a non-interactive (random) string oblivious transfer protocol and a 2-round chosen message string oblivious transfer. •We prove a 1-round communication lower bound for BEC under certain conditions. Central to our technical contributions is the abstraction of a distributional variant of the random ABB functionality. Investigating the concrete efficiency of founding MPC from this primitive leads to fascinating new mathematical challenges in well-established MPC models, which will be of broader interest to the community.

The offline-online model is a leading paradigm for practical secure multi-party computation (MPC) protocol design that has successfully reduced the overhead for several prevalent privacy-preserving computation functionalities common to diverse application domains. However, the prohibitive overheads associated with secure comparison – one of these vital functionalities – often bottlenecks current and envisioned MPC solutions. Indeed, an efficient secure comparison solution has the potential for significant real-world impact through its broad applications.This work identifies and presents SIM, a secure protocol for the functionality of interval membership testing. This security functionality, in particular, facilitates secure less-than-zero testing and, in turn, secure comparison. A key technical challenge is to support a fast online protocol for testing in large integer rings while keeping the precomputation tractable. Motivated by the map-reduce paradigm, this work introduces the innovation of (1) computing a sequence of intermediate functionalities on a partition of the input into input blocks and (2) securely aggregating the output from these intermediate outputs. This innovation allows controlling the size of the precomputation through a granularity parameter representing these input blocks’ size – enabling application-specific automated compiler optimizations.To demonstrate our protocols’ efficiency, we implement and test their performance in a high-demand application: privacy-preserving machine learning. The benchmark results show that switching to our protocols yields significant performance improvement, which indicates that using our protocol in a plug-and-play fashion can improve the performance of various security applications. Our new paradigm of protocol design may be of independent interest because of its potential for extensions to other functionalities of practical interest.

Abstract While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions. We implement our protocols over the HoneyBadgerMPC library and apply it to two prominent secure computation tasks: privacy-preserving evaluation of decision trees and privacy-preserving evaluation of Markov processes. For the decision tree evaluation problem, we demonstrate the feasibility of evaluating high-depth decision tree models in a generaln-party setting. For the Markov process application, we demonstrate that Poly-math can compute large powers of transition matrices with better online time and less communication.