Privacy Preservation in Online Social Networks Using Graph-Attribute-Driven Optimal Clustering Algorithm
Keywords:
Privacy preservation, online social networks, Sensitive information, Risk mitigation, anonymization degree, information loss and execution time.Abstract
The surge in global online social network (OSN) users, especially post-COVID-19, has made it an integral part of daily life. As more rely on social networks, there's an increasing demand for privacy protection because these platforms store sensitive user information. This creates risks of potential intruders trying to access and misuse that information. The research aims to create a method for safeguarding privacy in OSN, minimizing the risk of sensitive information leaks, reducing processing time, and achieving high-level privacy preservation with minimal information loss (IL). The outcomes show that, in terms of privacy preservation, the suggested strategy performs better than cutting-edge techniques. The metrics employed, including anonymization degree, IL, and execution time (ET), demonstrate the high-level effectiveness of the proposed approach in achieving the specified privacy goals. The proposed approach effectively anonymizes OSNs through Graph-Attribute-Driven Optimal Clustering Algorithm (GADOCA); the research offers a practical solution to the ongoing challenges in ensuring k-anonymity, l-diversity, and t-closeness. The evaluation against real-world data adds credibility to the proposed method, emphasizing its potential to enhance privacy preservation in OSNs and contribute to the broader field of information security.
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