Differentially Private Gaussian Processes

2 Jun 2016Michael Thomas SmithMax ZwiesseleNeil D. Lawrence

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the differential privacy framework with the flexibility of Gaussian processes (GPs)... (read more)

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