Secure computation enables mutually distrusting parties to jointly evaluate a function on their private inputs without revealing anything but the function's output. Generic secure computation protocols in the semi-honest model have been studied extensively and several best practices have evolved. In this work, we design and implement a mixed-protocol framework, called ABY, that efficiently combines secure computation schemes based on Arithmetic sharing, Boolean sharing, and Yao's garbled circuits and that makes available best practice solutions in secure two-party computation. Our framework allows to pre-compute almost all cryptographic operations and provides novel, highly efficient conversions between secure computation schemes based on pre-computed oblivious transfer extensions. ABY supports several standard operations and we perform benchmarks on a local network and in a public intercontinental cloud. From our benchmarks we deduce new insights on the efficient design of secure computation protocols, most prominently that oblivious transfer-based multiplications are much more efficient than multiplications based on homomorphic encryption. We use ABY to construct mixed-protocols for three example applications-private set intersection, biometric matching, and modular exponentiation-and show that they are more efficient than using a single protocol. Keywords-secure two-party computation; mixed-protocols; efficient protocol design Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

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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.

While secure multi-party computation (MPC) is a vibrant research topic and a multitude of practical MPC applications have been presented recently, their development is still a tedious task that requires expert knowledge. Previous works have made first steps in compiling high-level descriptions from various source descriptions into MPC protocols, but only looked at a limited set of protocols. In this work we present HyCC, a tool-chain for automated compilation of ANSI C programs into hybrid protocols that efficiently and securely combine multiple MPC protocols with optimizing compilation, scheduling, and partitioning. As a result, our compiled protocols are able to achieve performance numbers that are comparable to hand-built solutions. For the MiniONN neural network (Liu et al., CCS 2017), our compiler improves performance of the resulting protocol by more than a factor of 3. Thus, for the first time, highly efficient hybrid MPC becomes accessible for developers without cryptographic background.

The growing relevance of Internet eXchange Points (IXPs), where an increasing number of networks exchange routing information, poses fundamental questions regarding the privacy guarantees of confidential business information. To facilitate the exchange of routes among their members, IXPs provide Route Server (RS) services to dispatch the routes according to each member's export policies. Nowadays, to make use of RSes, these policies must be disclosed to the IXP. This state of affairs raises privacy concerns among network administrators and even deters some networks from subscribing to RS services. We design sixpack (which stands for "Securing Internet eXchange Points Against Curious onlooKers"), a RS service that leverages Secure Multi-Party Computation (SMPC) techniques to keep export policies confidential, while maintaining the same functionalities as today's RSes. We assess the effectiveness and scalability of our system by evaluating our prototype implementation and using traces of data from one of the largest IXPs in the world.

An important initialization step in many social-networking applications is contact discovery, which allows a user of the service to identify which of its existing social contacts also use the service. Naïve approaches to contact discovery reveal a user’s entire set of social/professional contacts to the service, presenting a significant tension between functionality and privacy. In this work, we present a system for private contact discovery, in which the client learns only the intersection of its own contact list and a server’s user database, and the server learns only the (approximate) size of the client’s list. The protocol is specifically tailored to the case of a small client set and large user database. Our protocol has provable security guarantees and combines new ideas with state-of-the-art techniques from private information retrieval and private set intersection. We report on a highly optimized prototype implementation of our system, which is practical on real-world set sizes. For example, contact discovery between a client with 1024 contacts and a server with 67 million user entries takes 1.36 sec (when using server multi-threading) and uses only 4.28 MiB of communication.

Abstract:The Border Gateway Protocol (BGP) computes routes between the organizational networks that make up today's Internet. Unfortunately, BGP suffers from deficiencies, including slow convergence, security problems, a lack of innovation, and the leakage of sensitive information about domains' routing preferences. To overcome some of these problems, we revisit the idea of centralizing and using secure multi-party computation (MPC) for interdomain routing which was proposed by Gupta et al. (ACM HotNets'12). We implement two algorithms for interdomain routing with state-of-the-art MPC protocols. On an empirically derived dataset that approximates the topology of today's Internet (55 809 nodes), our protocols take as little as 6 s of topologyindependent precomputation and only 3 s of online time. We show, moreover, that when our MPC approach is applied at country/region-level scale, runtimes can be as low as 0.17 s online time and 0.20 s pre-computation time. Our results motivate the MPC approach for interdomain routing and furthermore demonstrate that current MPC techniques are capable of efficiently tackling real-world problems at a large scale.

We present MOTION, an efficient and generic open-source framework for mixed-protocol secure multi-party computation (MPC) . MOTION is built in a user-friendly, modular, and extensible way, intended to be used as a tool in MPC research and to increase adoption of MPC protocols in practice. Our framework incorporates several important engineering decisions such as full communication serialization, which enables MPC over arbitrary messaging interfaces and removes the need of owning network sockets. MOTION also incorporates several performance optimizations that improve the communication complexity and latency, e.g., \( 2\times \)  better online round complexity of precomputed correlated  Oblivious Transfer (OT) . We instantiate our framework with protocols for N  parties and security against up to \( N-1 \) passive corruptions: the MPC protocols of Goldreich-Micali-Wigderson (GMW) in its arithmetic and Boolean version and OT-based BMR (Ben-Efraim et al., CCS’16), as well as novel and highly efficient conversions between them, including a non-interactive conversion from BMR to arithmetic GMW. MOTION is highly efficient, which we demonstrate in our experiments. Compared to secure evaluation of AES-128 with \( N=3 \) parties in a high-latency network with OT-based BMR, we achieve a 16 \( \times \) better throughput of 16 AES evaluations per second using BMR. With this, we show that BMR is much more competitive than previously assumed. For \( N=3 \) parties and full-threshold protocols in a LAN, MOTION is \( 10\times \) – \( 18\times \) faster than the previous best passively secure implementation from the MP-SPDZ framework, and \( 190\times \) – \( 586\times \) faster than the actively secure SCALE-MAMBA framework. Finally, we show that our framework is highly efficient for privacy-preserving neural network inference.