Survey on Privacy-Preserving Techniques for Data Publishing

TL;DR

This survey reviews privacy-preserving techniques for microdata de-identification, discussing challenges, approaches, and measures to balance privacy protection with data utility in response to growing privacy concerns.

Contribution

It provides a comprehensive overview of existing techniques, taxonomies, and theoretical analyses of privacy-preserving methods in data publishing.

Findings

Identifies key challenges in balancing privacy and data utility.

Reviews various privacy measures and their effectiveness.

Highlights open issues and future research directions.

Abstract

The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques. However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individuals' privacy while maintaining the interpretability of the data, i.e. its usefulness. Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing privacy-preserving techniques used in microdata de-identification, privacy measures suitable for several disclosure types and, information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review taxonomies of privacy-preserving techniques, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.