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Differential Privacy offers strong guarantees such as immutable privacy underpost processing. Thus it is often looked to as a solution to learning onscattered and isolated data. This work focuses on supervised manifold learning,a paradigm that can generate fine-tuned manifolds for a target use case. Ourcontributions are two fold. 1) We present a novel differentially private method\textit{PrivateMail} for supervised manifold learning, the first of its kind toour knowledge. 2) We provide a novel private geometric embedding scheme for ourexperimental use case. We experiment on private "content based image retrieval"- embedding and querying the nearest neighbors of images in a private manner -and show extensive privacy-utility tradeoff results, as well as thecomputational efficiency and practicality of our methods.

PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval