AI-Driven Hybrid Privacy Preservation Model For Enhanced Security In Online Social Networks

This paper discusses the design and testing of an AI-Driven Hybrid Privacy Preservation Model, developed for online social networks. The model incorporates advanced AI techniques with cryptographic mechanisms and adaptive management of privacy to enhance security and privacy in OSNs. AI-HPPM considers privacy threat detection and mitigation. Machine learning algorithms are used in order to distinguish between legit activities of the user and suspicious activities of the user. This also makes use of homomorphic encryption to protect user data while processing with no high overhead in performance. Adaptive privacy settings also update automatically in relation to user behavior and context changes greatly reducing manual intervention. Comprehensive testing revealed that AI-HPPM has exhibited better performance when having 95.8% detection rate for threats, a minimal overhead of 2.5% cryptographic, and an adaptability rate of 93%.The model proactively alerts the users to potential risks with an 85% user response rate toward privacy alerts that effectively prevent breach of privacy before incidents arise. AI-HPPM outperforms conventional models in the realms of accuracy, efficacy, and degree of user satisfaction. This hybrid approach provides a comprehensive, future-proof solution for privacy protection in rapidly evolving digital environments. Thus, the paper concludes that AI-HPPM is a scalable, adaptable, and efficient model for enhancing privacy in OSNs and beyond.