An End-to-End System for Large Scale P2P MPC-as-a-Service and Low-Bandwidth MPC for Weak Participants

Barak, Assi; Hirt, Martin; Koskas, Lior; Lindell, Yehuda

doi:10.1145/3243734.3243801

Cited by 30 publications

(17 citation statements)

References 21 publications

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“…It is allowed that the offline phase is more costly as it can be run for a long time before the online phase. For the offline phase, many state-of-the-art offline protocols [4,7] are perfectly compatible with our solutions. .…”

Section: In the Malicious Adversary Setting [S]mentioning

confidence: 90%

“…The communication complexity of each of above operations could be linear with number of parties if we pick proper sub-protocols (e.g. [4]). The overall round complexity is O(log(max(e i ))+log(n)) considering both the computation of [r ei ] and the multiplication of all [r ei i ] can be parallelized.…”

Section: Offline Phasementioning

confidence: 99%

“…Secure multi-party computation (MPC) [4,8,32] enables mutually distrusting parties to compute securely over their private data. Informally, in a system of n > 1 mutually distrusting parties, an MPC protocol allows them to "securely" evaluate any agreed-on function f of their private inputs, in the presence of a centralized adversary controlling at most any t < n parties 1 .…”

Section: Introductionmentioning

confidence: 99%

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Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

Kate

et al. 2021

Proceedings on Privacy Enhancing Technologies

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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 general n-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.

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Section: In the Malicious Adversary Setting [S]mentioning

confidence: 90%

Section: Offline Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

Kate

et al. 2021

Proceedings on Privacy Enhancing Technologies

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“…Nevertheless, benchmarks can provide insight into a framework's strengths and weaknesses, and do have value if they are not used as a sole measure of a framework's contribution. Recent work by Barak et al [8] provides an approach for performance comparison between frameworks with compatible architectures.…”

Section: Standardization and Benchmarksmentioning

confidence: 99%

SoK: General Purpose Compilers for Secure Multi-Party Computation

Hastings

Hemenway

Noble

et al. 2019

2019 IEEE Symposium on Security and Privacy (SP)

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Secure multi-party computation (MPC) allows a group of mutually distrustful parties to compute a joint function on their inputs without revealing any information beyond the result of the computation. This type of computation is extremely powerful and has wide-ranging applications in academia, industry, and government. Protocols for secure computation have existed for decades, but only recently have general-purpose compilers for executing MPC on arbitrary functions been developed. These projects rapidly improved the state of the art, and began to make MPC accessible to non-expert users. However, the field is changing so rapidly that it is difficult even for experts to keep track of the varied capabilities of modern frameworks.In this work, we survey general-purpose compilers for secure multi-party computation. These tools provide high-level abstractions to describe arbitrary functions and execute secure computation protocols. We consider eleven systems: EMP-toolkit, Obliv-C, ObliVM, TinyGarble, SCALE-MAMBA (formerly SPDZ), Wysteria, Sharemind, PICCO, ABY, Frigate and CBMC-GC. We evaluate these systems on a range of criteria, including language expressibility, capabilities of the cryptographic back-end, and accessibility to developers. We advocate for improved documentation of MPC frameworks, standardization within the community, and make recommendations for future directions in compiler development. Installing and running these systems can be challenging, and for each system, we also provide a complete virtual environment (Docker container) with all the necessary dependencies to run the compiler and our example programs.

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“…An important research focus in making MPCaaS practical has been to reduce the necessary trust assumptions to a minimum. Malicious-case security for confidentiality and integrity guarantees has now become a standard feature of most implementations [8], [9], [10], [11], and protocols like Honeybad-gerMPC [4] and Blinder [12] furthermore guarantee availability in this setting as well.…”

Section: Introductionmentioning

confidence: 99%

Publicly Auditable MPC-as-a-Service with succinct verification and universal setup

Kanjalkar¹,

Zhang²,

Gandlur³

et al. 2021

Preprint

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In recent years, multiparty computation as a service (MPCaaS) has gained popularity as a way to build distributed privacy-preserving systems like blockchain trusted parameter setup ceremonies, and digital asset auctions. We argue that for many such applications, we should also require that the MPC protocol is publicly auditable, meaning that anyone can check the given computation is carried out correctly -even if the server nodes carrying out the computation are all corrupt. In a nutshell, the way to make an MPC protocol auditable is to combine an underlying MPC protocol with a verifiable computing proof (in particular, a SNARK). Building a general purpose MPCaaS from existing constructions would require us to perform a costly "trusted setup" every time we wish to run a new or modified application. To address this, we provide the first efficient construction for auditable MPC that has a one time universal setup. Despite improving the trusted setup, we match the stateof-the-art in asymptotic performance: the server nodes incur a linear computation overhead and constant round communication overhead compared to the underlying MPC, and the audit size and verification are logarithmic in the application circuit size. We also provide an implementation and benchmarks that support our asymptotic analysis in example applications. Furthermore, compared with existing auditable MPC protocols, besides offering a universal setup our construction also has a 3x smaller proof, 3x faster verification time and comparable prover time.

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An End-to-End System for Large Scale P2P MPC-as-a-Service and Low-Bandwidth MPC for Weak Participants

Cited by 30 publications

References 21 publications

Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

SoK: General Purpose Compilers for Secure Multi-Party Computation

Publicly Auditable MPC-as-a-Service with succinct verification and universal setup

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