GCSA Codes With Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication

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