[2510.14894] Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning

Computer Science > Cryptography and Security arXiv:2510.14894 (cs) [Submitted on 16 Oct 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning Authors:Marc Damie, Florian Hahn, Andreas Peter, Jan Ramon View a PDF of the paper titled Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning, by Marc Damie and Florian Hahn and Andreas Peter and Jan Ramon View PDF HTML (experimental) Abstract:To preserve data privacy, multi-party computation (MPC) enables executing Machine Learning (ML) algorithms on private data. However, MPC frameworks do not include optimized operations on sparse data. This absence makes them unsuitable for ML applications involving sparse data; e.g., recommender systems or genomics. Even in plaintext, such applications involve high-dimensional sparse data, that cannot be processed without sparsity-related optimizations due to prohibitively large memory requirements. Since matrix multiplication is a central building block of ML algorithms, our work proposes dedicated MPC algorithms to multiply secret-shared sparse matrices. Our sparse algorithms have several advantages over secure dense matrix multiplications (i.e., the classic multiplication). On the one hand, they avoid the memory issues caused by the "dense" data representation of dense multiplications. On the other hand, our algorithms can significantly reduc...