2020/02/18 by Zhen Chen, Zhe Chen, Zhuqing Jia +6 · 5 citations
Computer Science · Mathematics · #Advanced Data Storage Technologies #Cooperative Communication and Network Coding #Cryptography and Security (cs.CR) #Distributed #Error Correcting Code Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Parallel #and Cluster Computing (cs.DC) #cs.CR #cs.DC #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.2002.07750
openalex publication_date 2020/02/18 · arxiv created 2020/09/06 · arxiv updated 2020/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. The idea of noise alignment can also be combined with N-source Cross Subspace Alignment (N-CSA) codes and fast matrix multiplication algorithms like Strassen's construction. Moreover, noise alignment can be applied to symmetric secure private information retrieval to achieve the asymptotic capacity.