Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption

28 Jan 2020 Peter Fenner Edward O. Pyzer-Knapp

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data... (read more)

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