Publication

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

differentially private supervised manifold learning with applications like private image retrieval

Feb. 19, 2021

Groups

Vepakomma, Praneeth et al. Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. arXiv:2102.10802v1 [cs.LG] 22 Feb 2021

Abstract

Differential Privacy offers strong guarantees such as immutable privacy under post processing. Thus it is often looked to as a solution to learning on scattered and isolated data. This work focuses on supervised manifold learning, a paradigm that can generate fine-tuned manifolds for a target use case. Our contributions are two fold. 1) We present a novel differentially private method PrivateMail for supervised manifold learning, the first of its kind to our knowledge. 2) We provide a novel private geometric embedding scheme for our experimental 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 the computational efficiency and practicality of our methods. 

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