Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecture

TL;DR

This paper explores integrating swarm intelligence with differential privacy within the DIKW architecture to enhance privacy protection and computational efficiency in data processing.

Contribution

It introduces a novel approach combining swarm intelligence and differential privacy tailored for the DIKW framework, improving privacy and efficiency.

Findings

Swarm intelligence effectively reduces DIKW items used in privacy mechanisms.

The approach accelerates differential privacy processing across multiple DIKW levels.

Experimental results demonstrate improved computational efficiency.

Abstract

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm Intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-sourse IRIS dataset. This experiment demonstrates the efficiency of Swarm Intelligence in reducing computing complexity.