Automated Synthesis of Optimized Circuits for Secure Computation

Abstract

In the recent years, secure computation has been the subject of intensive research, emerging from theory to practice. In order to make secure computation usable by non-experts, Fairplay (USENIX Security 2004) initiated a line of research in compilers that allow to automatically generate circuits from high-level descriptions of the functionality that is to be computed securely. Most recently, TinyGarble (IEEE S&P 2015) demonstrated that it is natural to use existing hardware synthesis tools for this task. In this work, we present how to use industrial-grade hardware synthesis tools to generate circuits that are not only optimized for size, but also for depth. These are required for secure computation protocols with non-constant round complexity. We compare a large variety of circuits generated by our toolchain with hand-optimized circuits and show reduction of depth by up to 14%. The main advantages of our approach are developing customized libraries of depth-optimized circuit constructions which we map to high-level functions and operators, and using existing libraries available in the industrial-grade logic synthesis tools which are heavily tested. In particular, we show how to easily obtain circuits for IEEE 754 compliant floating-point operations. We extend the open-source ABY framework (NDSS 2015) to securely evaluate circuits generated with our toolchain and show between 0.5 to 21.4 times faster floating-point operations than previous protocols of Aliasgari et al. (NDSS 2013), even though our protocols work for two parties instead of three or more. As application we consider privacy-preserving proximity testing on Earth.

HyCC

“…Previous MPC compilers either only targeted a single class of protocols, e.g., Yao's garbled circuits [26,39,48], the GMW protocol [9,17], or linear secret-sharing-based MPC [7], or the compilers required the developer to use specific annotations to mark which protocol is used for each statement, e.g., [18,24]. The only other compiler that addresses the compilation of a program using two MPC protocols (Yao's gabled circuits and arithmetic sharing) is EzPC [14].…”

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

“…Moreover, it supports outsourcing of the computation to Semi-Trusted Third Parties (STTPs) and Single Instruction Multiple Data (SIMD) gates [60]. In principle, ABY also allows to compute very complex distance metrics that require logarithm or exponentiation operations using IEEE 754 Floating Point numbers [61], but this will slow down the protocols significantly and thus will not be considered in this paper.…”

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

“…The IEEE 754 floating-point representation is the standard choice used by virtually every numerical computation software because of its high accuracy and numerical stability when working with numbers across a wide range of orders of magnitude. However, efficient implementations of IEEE 754-compliant secure MPC protocols are a subject of on-going research [17,61], with the current state-of-the-art yielding 32 bit floating-point multiplication circuits running in time comparable to computers from the 1960's [68]. Unfortunately, these implementations do not yet scale to the throughput required for data analysis tasks involving large datasets, and forces us to rely on fixed-point encodings like previous work in this area [10,35,59].…”