Distributed privacy preservation for online social network using flexible clustering and whale optimization algorithm

Over the past few years, global use of Online Social Networks (OSNs) has increased. The rising use of OSN makes protecting users' privacy from OSN attacks difficult. Finally, it affects the basic commitment to protect OSN users from such invasions. The lack of a distributed, dynamic, and artificial intelligence (AI)-based privacy-preserving strategy for performance trade-offs is a research challenge. We propose the Distributed Privacy Preservation (DPP) for OSN using Artificial Intelligence (DPP-OSN-AI) to reduce Information Loss (IL) and improve privacy preservation from different OSN threats. DPP-OSN-AI uses AI to design privacy notions in distributed OSNs. DPP-OSN-AI consists of AI-based clustering, l-diversity, and t-closeness phases to achieve the DPP for OSN. The AI-based clustering is proposed for dynamic and optimal clustering of OSN users to ensure personalized k-anonymization to protect from AI-based threats. First, the optimal number of clusters is discovered dynamically with simple computations, and then the Whale Optimization Algorithm is designed to optimally place the OSN users across the clusters such that it helps to protect them from AI-based threats. Because k-anonymized OSN clusters are insufficient to handle all privacy concerns in a distributed OSN environment, we systematically applied the l-diversity privacy idea followed by the t-closeness to it, resulting in higher DPP and lower IL. The DPP-OSN-AI model is assessed for IL Efficiency (ILE), Degree of Anonymization (DoA,) and computational complexity using publically accessible OSN datasets. Compared to state-of-the-art, DPP-OSN-AI model DoA is 15.57% higher, ILE is 17.85% higher, and computational complexity is 3.61% lower.