Recent Results in Scalable Multi-Party Computation

Saia, Jared; Zamani, Mahdi

doi:10.1007/978-3-662-46078-8_3

Cited by 35 publications

(32 citation statements)

References 55 publications

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“…Algorithms exist for completely generic secure computations, Saia and Zamani give a comprehensive overview with a focus on scalability [9]. However, due to their focus on generic computations, these approaches are relatively complex and in the context of our application they still do not scale well enough and do not tolerate dynamic membership either.…”

Section: Related Workmentioning

confidence: 99%

Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation

Danner

Berta

Hegedűs

et al. 2018

Security and Communication Networks

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Privacy and security are among the highest priorities in data mining approaches over data collected from mobile devices. Fully distributed machine learning is a promising direction in this context. However, it is a hard problem to design protocols that are efficient yet provide sufficient levels of privacy and security. In fully distributed environments, secure multiparty computation (MPC) is often applied to solve these problems. However, in our dynamic and unreliable application domain, known MPC algorithms are not scalable or not robust enough. We propose a light-weight protocol to quickly and securely compute the sum query over a subset of participants assuming a semihonest adversary. During the computation the participants learn no individual values. We apply this protocol to efficiently calculate the sum of gradients as part of a fully distributed minibatch stochastic gradient descent algorithm. The protocol achieves scalability and robustness by exploiting the fact that in this application domain a "quick and dirty" sum computation is acceptable. We utilize the Paillier homomorphic cryptosystem as part of our solution combined with extreme lossy gradient compression to make the cost of the cryptographic algorithms affordable. We demonstrate both theoretically and experimentally, based on churn statistics from a real smartphone trace, that the protocol is indeed practically viable.

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Section: Related Workmentioning

confidence: 99%

Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation

Danner

Berta

Hegedűs

et al. 2018

Security and Communication Networks

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“…The actively malicious case mandates the availability of Byzantine consensus as a building block [51]. 6 In practical applications, the inputs typically consist of sets of values that were given to the peers P by external clients.…”

Section: Application To Smcmentioning

confidence: 99%

Byzantine set-union consensus using efficient set reconciliation

Dold

Grothoff

2017

EURASIP J. on Info. Security

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Applications of secure multiparty computation such as certain electronic voting or auction protocols require Byzantine agreement on large sets of elements. Implementations proposed in the literature so far have relied on state machine replication and reach agreement on each individual set element in sequence. We introduce set-union consensus, a specialization of Byzantine consensus that reaches agreement over whole sets. This primitive admits an efficient and simple implementation by the composition of Eppstein's set reconciliation protocol with Ben-Or's ByzConsensus protocol. A free software implementation of this construction is available in GNUnet. Experimental results indicate that our approach results in an efficient protocol for very large sets, especially in the absence of Byzantine faults. We show the versatility of set-union consensus by using it to implement distributed key generation, ballot collection, and cooperative decryption for an electronic voting protocol implemented in GNUnet. This is a revised and extended version of a paper published under the same title at ARES 2016.

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“…BGW exploits the fact that Shamir secret sharing admits basic operation such as addition and multiplication (at the cost of some communication among nodes). There have been some efforts to improve the efficiency of MPC algorithms, but mainly focusing on the communication loads (see [9]). This is an extended version of the paper, partially presented in IEEE Communication Theory Workshop (CTW), May 2018, and IEEE International Symposium on Information Theory (ISIT), June 2018 [1].…”

Section: Introductionmentioning

confidence: 99%

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

Nodehi

Maddah-Ali

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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In this paper, we consider a secure multi-party computation problem (MPC), where the goal is to offload the computation of an arbitrary polynomial function of some massive private matrices to a network of workers. The workers are not reliable, some may collude to gain information about the input data. The system is initialized by sharing a (randomized) function of each input matrix to each server. Since the input matrices are massive, the size of each share is assumed to be at most 1/k fraction of the input matrix, for some k ∈ N. The objective is to minimize the number of workers needed to perform the computation task correctly, such that even if an arbitrary subset of t − 1 workers, for some t ∈ N, collude, they cannot gain any information about the input matrices. We propose a sharing scheme, called polynomial sharing, and show that it admits basic operations such as adding and multiplication of matrices, and transposing a matrix. By concatenating the procedures for basic operations, we show that any polynomial function of the input matrices can be calculated, subject to the problem constraints. We show that the proposed scheme can offer order-wise gain, in terms of number of workers needed, compared to the approaches formed by concatenation of job splitting and conventional MPC approaches.

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Recent Results in Scalable Multi-Party Computation

Cited by 35 publications

References 55 publications

Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation

Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation

Byzantine set-union consensus using efficient set reconciliation

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

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