Investigate paper on sparse matrix-multiplication in HE

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.