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

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.

Bridging the Gap between Purpose-Driven Frameworks and Artificial General Intelligence

“…Finally, wisdom (W) is shaped as a result of the effective utilization of knowledge. The addition of purpose (P) serves as the driving force behind this transformation from data to wisdom, ensuring the alignment of AGI's computational capacities with human-centric needs [3][4][5].…”

Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition

“…where A is the transfer score matrix; A y i−1 ,y i is the score of the label y i−1 transfer to the label y i ; P is the character label score matrix obtained from the output of the BiLSTM layer, and P i,y i is the score of the i-th character predicted as y i by the BiLSTM layer. Then, the probability distribution of the label sequence y is obtained by normalizing S(x, y) with the softmax function, which can be calculated as Equation (16):…”

“…Nonetheless, these methods may not prioritize the significance of the intended purpose and might not make the most of entities within Chinese electronic medical records, potentially leading to the exclusion of crucial lexical information. To address these gaps, this paper adopts the novel purposedriven DIKW [16], which connects the diverse models of DIKW through purpose and unifies them as a whole.…”

The Role of DIKWP Assets in Shaping Economic Systems and Implications for an Intelligent Consultant System in Real Estate Investment

“…It also supports objective DIKWP resources, to perform meta-model representation of DIKWP service identification and transformation . In addition, the service-oriented construction of DIKWP transformation (Lei & Yucong, 2021) as a mechanism for privacy protection under the combination scenarios (Li et al, 2021) of smart form filling , physics (Li et al, 2023), law (Mei et al, 2022;Ngo et al, 2024), cloud computing (Song et al, 2018), and edge computing (Gao et al, 2021;Yin et al, 2024) promotes the synergistic development of the digital economy. The process has not yet been formalized to adapt with logic of practical applications (Sakama et al, 2018).…”