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.