Related papers: Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

URL: http://arxiv.org/abs/2102.10802v1
Date: Mon, 22 Feb 2021 06:58:46 GMT
Title: Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval
Authors: Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Abstract summary: We present a novel differentially private method textitPrivateMail for supervised manifold learning. We show extensive privacy-utility tradeoff results, as well as the computational efficiency and practicality of our methods.
Score: 14.93584434176082
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 \textit{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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