Investigate paper on sparse matrix-multiplication in HE

[j2kun]

This paper came across my feed that implements sparse matrix-vector multiplication.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5580098

Efficient Privacy-Preserving Sparse Matrix-Vector Multiplication Using Homomorphic Encryption Yang Gao Gang Quan Wujie Wen Scott Piersall Qian Lou Liqiang Wang

Abstract Sparse matrix-vector multiplication (SpMV) is a fundamental operation in scientific computing, data analysis, and machine learning. When processing sensitive data, ensuring privacy becomes critically important. Although homomorphic encryption (HE) enables privacy-preserving computation, its application to SpMV has remained largely unaddressed. To the best of our knowledge, this paper presents the first framework that efficiently integrates HE with SpMV, addressing the dual challenges of computational efficiency and data security. In particular, we introduce a novel compressed matrix format: Compressed Sparse Sorted Column (CSSC), which is specifically designed to optimize encrypted sparse matrix computations. By preserving sparsity and enabling efficient ciphertext packing, CSSC significantly reduces storage and computational overhead. Our experimental results on real-world datasets demonstrate that the proposed method achieves significant gains in both processing time and memory usage. This study advances privacy-preserving SpMV and lays the groundwork for secure applications in federated learning, encrypted databases, and scientific computing, and beyond.

[j2kun]

tbh seems like a lower priority right now since we are focusing on ML which is all dense, but still could be interesting to see what the potential for speedup is in the sparse case.