Visible to the public GCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication

TitleGCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication
Publication TypeConference Paper
Year of Publication2020
AuthorsChen, Z., Jia, Z., Wang, Z., Jafar, S. A.
Conference Name2020 IEEE International Symposium on Information Theory (ISIT)
Date PublishedJune 2020
ISBN Number978-1-7281-6432-8
Keywordsbatch processing, codes, coding theory, colluding servers, compositionality, computational complexity, computer network security, Cross Subspace Alignment codes, cryptography, flexible interserver network topology, GCSA codes, GCSA-NA, master gains no additional information, matrix multiplication, Metrics, network servers, noise alignment, pairwise products, polynomial sharing, polynomials, pubcrawl, resilience, Resiliency, secure interserver communication, secure multiparty batch matrix multiplication problem, security, SMBMM, synchronisation, telecommunication network topology

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes. The state of art solution to SMBMM is a coding scheme called polynomial sharing (PS) that was proposed by Nodehi and Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects - more efficient and secure inter-server communication, lower latency, flexible inter-server network topology, efficient batch processing, and tolerance to stragglers.

Citation Keychen_gcsa_2020