ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2105.04045
21
13
v1v2v3 (latest)

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

9 May 2021
Yingbo Li
Yucong Duan
Z. Maamar
Haoyang Che
Anamaria-Beatrice Spulber
Stelios Fuentes
ArXiv (abs)PDFHTML
Abstract

Privacy protection has recently attracted the attention of both academics and industries. Society protects individual data privacy through complex legal frameworks. This has become a topic of interest with the increasing applications of data science and artificial intelligence that have created a higher demand to the ubiquitous application of the data. The privacy protection of the broad Data-InformationKnowledge-Wisdom (DIKW) landscape, the next generation of information organization, has not been in the limelight. Next, we will explore DIKW architecture through the applications of 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 could effectively optimize and reduce the number of items in DIKW used in differential privacy, this way accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is proved 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.

View on arXiv
Comments on this paper