Threat Analysis of Insider Collusion Attacks On Kernel-Based Privacy Preserving Data-Mining

This study addressed insider collusion risks in kernel-based privacy-preserving data mining. By combining user authentication, secure data storage, privacy protection, and threat detection, the system effectively identifies suspicious activities and prevents unauthorized access. Key Outcomes: ·      Improved detection of insider collusion attacks. ·      Protection of sensitive information during data mining. ·      Enhanced system reliability and security. Future Work: ·      Integration of AI/ML for real-time threat detection. ·      Exploration of blockchain for secure and tamper-proof distributed data storage.     The proposed framework provides a secure, privacy-aware environment for analyzing large datasets while minimizing insider threats.

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