ORCID Identifiers
Document Type
Article
Source of Publication
Mobile Information Systems
Publication Date
6-28-2021
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-source IRIS dataset. This experiment demonstrates the efficiency of swarm intelligence in reducing computing complexity.
DOI Link
ISSN
Publisher
Hindawi
Volume
2021
Disciplines
Computer Sciences
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Li, Yingbo; Duan, Yucong; Maamar, Zakaria; Che, Haoyang; Spulber, Anamaria-Beatrice; and Fuentes, Stelios, "Swarm Differential Privacy for Purpose-Driven Data-Information-Knowledge-Wisdom Architecture" (2021). All Works. 4392.
https://zuscholars.zu.ac.ae/works/4392
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series