Group Anonymity

TL;DR

This paper introduces a wavelet-based method to enhance group anonymity in collective data, addressing a less-explored aspect of data privacy protection with practical application.

Contribution

It proposes a novel wavelet-based approach for group anonymity, filling a gap in statistical disclosure control for collective data.

Findings

Effective wavelet-based method demonstrated

Real-life example illustrates practical application

Enhances group privacy in collective datasets

Abstract

In recent years the amount of digital data in the world has risen immensely. But, the more information exists, the greater is the possibility of its unwanted disclosure. Thus, the data privacy protection has become a pressing problem of the present time. The task of individual privacy-preserving is being thoroughly studied nowadays. At the same time, the problem of statistical disclosure control for collective (or group) data is still open. In this paper we propose an effective and relatively simple (wavelet-based) way to provide group anonymity in collective data. We also provide a real-life example to illustrate the method.