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.

翻译：社会通过复杂的法律框架保护个人数据隐私; 数据科学和人工智能的应用越来越多,导致对数据普遍应用的需求增加; 广泛的数据-信息-知识-智慧(DIKW)景观的隐私保护,即下一代信息组织,发挥了次要作用; 在本文件中,我们将通过应用广受欢迎的群群情情报和差异性隐私来探索DIKW结构; 由于差异性隐私证明是一种有效的数据隐私方法,我们将从DIKW域的角度来看待这一问题; 快速情报可以有效地优化和减少用于不同隐私的DIKW物品数量,从而加快不同隐私在跨过概念-信息-知识-智慧(DIKW)多种模式方面的效力和效率。 拟议的方法通过应用基于开放性软体IRIS数据集的个人化数据来证明。 这一实验表明,Swarm情报在降低计算复杂性方面的效率。