Providing Group Anonymity Using Wavelet Transform

TL;DR

This paper introduces a novel wavelet transform-based method to enhance group data anonymity by redistributing approximation values while maintaining data mean and details, thus protecting collective data patterns.

Contribution

It presents a new approach using wavelet transform for group anonymity, focusing on redistributing approximation values without altering details or mean.

Findings

Effective in protecting group data patterns

Maintains data mean and details during anonymization

Simple and easy-to-implement method

Abstract

Providing public access to unprotected digital data can pose a threat of unwanted disclosing the restricted information. The problem of protecting such information can be divided into two main subclasses, namely, individual and group data anonymity. By group anonymity we define protecting important data patterns, distributions, and collective features which cannot be determined through analyzing individual records only. An effective and comparatively simple way of solving group anonymity problem is doubtlessly applying wavelet transform. It's easy-to-implement, powerful enough, and might produce acceptable results if used properly. In the paper, we present a novel method of using wavelet transform for providing group anonymity; it is gained through redistributing wavelet approximation values, along with simultaneous fixing data mean value and leaving wavelet details unchanged (or proportionally altering them). Moreover, we provide a comprehensive example to illustrate the method.