  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.
