Providing Data Group Anonymity Using Concentration Differences

TL;DR

This paper introduces a new method for protecting group data patterns to ensure data privacy, addressing the challenge of preventing undesirable information disclosure in publicly accessible digital data.

Contribution

The paper presents a novel approach for providing group data anonymity using concentration differences, filling a gap in existing data privacy techniques.

Findings

Proposed a new method for group data anonymity

Provided a comprehensive illustrative example

Addresses data privacy in public data sharing

Abstract

Public access to digital data can turn out to be a cause of undesirable information disclosure. That's why it is vital to somehow protect the data before publishing. There exist two main subclasses of such a task, namely, providing individual and group anonymity. In the paper, we introduce a novel method of protecting group data patterns. Also, we provide a comprehensive illustrative example.