Decouple-and-Sample: Protecting sensitive information in task agnostic data release

• URL: http://arxiv.org/abs/2203.13204v1
• Date: Thu, 17 Mar 2022 19:15:33 GMT
• Title: Decouple-and-Sample: Protecting sensitive information in task agnostic data release
• Authors: Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
• Abstract summary: sanitizer is a framework for secure and task-agnostic data release. We show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately.
• Score: 17.398889291769986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns. We alleviate these concerns by sanitizing datasets in a two-stage process. First, we introduce a global decoupling stage for decomposing raw data into sensitive and non-sensitive latent representations. Secondly, we design a local sampling stage to synthetically generate sensitive information with differential privacy and merge it with non-sensitive latent features to create a useful representation while preserving the privacy. This newly formed latent information is a task-agnostic representation of the original dataset with anonymized sensitive information. While most algorithms sanitize data in a task-dependent manner, a few task-agnostic sanitization techniques sanitize data by censoring sensitive information. In this work, we show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately. We validate the effectiveness of the sanitizer by outperforming state-of-the-art baselines on the existing benchmark tasks and demonstrating tasks that are not possible using existing techniques.

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