Network-Efficient Pipelining-Based Secure Multiparty Computation for Machine Learning Applications

Abstract

Secure multi-party computation (SMPC) allows mutually distrusted parties to evaluate a function jointly without revealing their private inputs. This technique helps organizations collaborate on a common goal without disclosing confidential or protected data. Despite its suitability for privacy-preserving computation, SMPC suffers from network-based performance limitations. Specifically, the SMPC parties perform the techniques in rounds, where they execute a local computation and then share their round output with the other parties. This network interchange creates a bottleneck as parties need to wait until the data propagates before resuming the execution. To reduce the SMPC execution time, we propose a pipelining-like approach for each round's computation and communication by dividing the data and readjusting the execution order. Targeting deep learning applications, we propose strategies for the case of matrix multiplication, a core component of such applications. Our results on a distributed cloud deployment show a significant reduction in the SMPC execution time.

Efficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation

“…Baum et al [24] first integrated Pedersen commitments [25] with an efficient information-theoretic SPDZ protocol [10] and proposed a protocol for which any party could function as an auditor, with approximately twice the efficiency of the original SPDZ protocol. Then, the work in [26][27][28][29] built on this foundation and proposed protocols with higher security requirements or efficiency. Our work utilized a single-party HV as the validator, which has the merit that the validation phase can be executed simultaneously with the computational protocol.…”

Efficient First-price Sealed E-auction Protocol Under Secure Multi-party Computational Malicious Model

“…Privacy computing is a collection of technologies that can analyze, compute, and integrate privacy data without revealing the privacy data itself, thus achieving the purpose of "usability and invisibility" of privacy data [5]. Compared with traditional data usage, privacy computing not only maximizes the security of private data, but also facilitates the integration of multiple data resources to maximize data value [6][7][8]. MPC is a multi-party private computation technique that does not require a trusted third party, and was proposed by Turing Award winner Andrew Chi-Chih Yao in 1982 to answer the millionaire problem [9].…”