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

• Main Authors: Vepakomma, Praneeth, Balla, Julia, Raskar, Ramesh
• Format: Journal Article
• Language: English
• Published: 22.02.2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Cryptography and Security; Computer Science - Databases; Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Learning
• DOI: 10.48550/arxiv.2102.10802

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.