Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection

TL;DR

This paper introduces RUCA, a privacy-preserving data projection method that balances utility and privacy, outperforming existing techniques in classification tasks while safeguarding private information.

Contribution

RUCA offers a flexible, compressive-privacy approach that enhances utility for intended tasks and reduces private information inference, addressing limitations of prior methods.

Findings

RUCA significantly outperforms existing privacy-preserving techniques.

It effectively balances utility and privacy across various privacy levels.

Experimental results on Census and Human Activity Recognition datasets validate its effectiveness.

Abstract

With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it useful for intended applications. Existing methods, however, do not offer enough flexibility for controlling the utility-privacy trade-off and may incur unfavorable results when privacy requirements are high. To tackle these drawbacks, we propose a compressive-privacy based method, namely RUCA (Ratio Utility and Cost Analysis), which can not only maximize performance for a privacy-insensitive classification task but also minimize the ability of any classifier to infer private information from the data. Experimental results on Census and Human Activity Recognition data sets demonstrate that RUCA significantly outperforms existing privacy preserving data projection techniques for a wide range of privacy pricings.