2021/06/15 by Elahe Vedadi, Vedadi, Elahe, Yasaman Keshtkarjahromi +3
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Distributed #FOS: Computer and information sciences #Information Theory (cs.IT) #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2106.08290
openalex publication_date 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We investigate the problem of privacy preserving distributed matrix multiplication in edge networks using multi-party computation (MPC). Coded multi-party computation (CMPC) is an emerging approach to reduce the required number of workers in MPC by employing coded computation. Existing CMPC approaches usually combine coded computation algorithms designed for efficient matrix multiplication with MPC. We show that this approach is not efficient. We design a novel CMPC algorithm; PolyDot coded MPC (PolyDot-CMPC) by using a recently proposed coded computation algorithm; PolyDot codes. We exploit "garbage terms" that naturally arise when polynomials are constructed in the design of PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. We show that entangled polynomial codes, which are consistently better than PolyDot codes in coded computation setup, are not necessarily better than PolyDot-CMPC in MPC setting.