SSA-Caterpillar in Group Anonymity

TL;DR

This paper introduces a novel SSA-based technique for ensuring group anonymity in statistical data, enhancing privacy preservation in demographic surveys by revealing hidden patterns and protecting group information.

Contribution

It proposes a new SSA-based method for group anonymity in statistical data, addressing a gap in existing privacy protection techniques.

Findings

SSA effectively reveals hidden demographic patterns.

The method enhances group privacy in data publishing.

It provides a new approach to data anonymization.

Abstract

Nowadays, it is a common practice to protect various types of statistical data before publishing them for different researches. For instance, when conducting extensive demographic surveys such as national census, the collected data should be at least depersonalized to guarantee proper level of privacy preservation. In practice, even more complicated methods of data protection need to be used. All these methods can be generally divided into two classes. The first ones aim at providing individual data anonymity, whereas the other ones are focused on protecting information about a group of respondents. In this paper, we propose a novel technique of providing group anonymity in statistical data using singular spectrum analysis (SSA).Also, we apply SSA to defining hidden patterns in demographic data distribution.