面向目的驱动的数据-信息-知识-智慧体系结构的Swarm差分隐私
Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecture
摘要：
隐私保护是近年来学术界和工业界关注的焦点。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，对数据的无处不在的应用提出了更高的要求。广泛的数据-信息-知识-智慧（DIKW）领域的隐私保护，即下一代信息组织，已成为次要角色。在本文中，我们将探讨DIKW架构，通过流行的群体智能和差分隐私的应用。由于差分隐私被证明是一种有效的数据隐私方法，我们将从DIKW域的角度来看待它。群体智能算法可以有效地优化和减少差分隐私中DIKW的项目数，从而提高了差分隐私在概念DIKW多模态交叉中的有效性和效率。所提出的方法是通过应用程序的个性化数据，是基于开源IRIS数据集。实验证明了群体智能在降低计算复杂度方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
