A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods.

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise or entail significant computational overhead. An example for such a use case is machine learning on clinical data. To realize exact and efficient privacy preserving computation of kernel methods, we propose FLAKE, a Framework for Learning with Anonymized KErnels on horizontally distributed data. With our method, the data sources mask their data so that a Gram matrix can be computed without compromising privacy or utility. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that our framework prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. The conducted experiments on clinical, genomic, and image data provide confirmation that our approach is applicable across a wide range of settings. Additionally, our method outperforms comparable approaches in both computational efficiency and accuracy. Thus, FLAKE is a lightweight, applicable approach suitable for various use cases.