Privacy-Preserving Kernel Computation For Vertically Partitioned Data
Mirko Polato, Alberto Gallinaro, Fabio Aiolli · ESANN 2021 proceedings · 2021
In this paper, we propose a secure and privacy-preserving technique for computing dot-product kernels on vertically distributed data.Our proposal is based on secure multi-party computation which provides theoretical guarantees on both security and privacy.We also provide a practical application of the method by adapting a kernel-based collaborative filtering technique to the federated setting.An extensive experimental evaluation shows the effectiveness of the proposed approach.11