Escience25

Our research paper, “Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption”, has been accepted for presentation at the 21th IEEE International Conference on e-Science (eScience). Recommendation systems need data — but at what privacy cost? We combine homomorphic encryption with sparse matrix tricks (CSR + FHE) to keep user data private and accurate, while reducing communication costs.