2023/05/13 by Lev Tauz, Lara Dolecek, Tauz, Lev +1
Computer Science · #Complexity and Algorithms in Graphs #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2305.07983
openalex publication_date 2023/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present a novel variation of the coded matrix multiplication problem which we refer to as fully private grouped matrix multiplication (FPGMM). In FPGMM, a master wants to compute a group of matrix products between two matrix libraries that can be accessed by all workers while ensuring that any number of prescribed colluding workers learn nothing about which matrix products the master desires, nor the number of matrix products. We present an achievable scheme using a variant of Cross-Subspace Alignment (CSA) codes that offers flexibility in communication and computation cost. Additionally, we demonstrate how our scheme can outperform naive applications of schemes used in a related privacy focused coded matrix multiplication problem.