Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism

Abstract

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.