🤖 AI Summary
This study addresses the challenge of balancing data privacy and analytical utility in threat intelligence sharing by systematically introducing differential privacy into cybersecurity analysis. The work proposes injecting calibrated noise into SIEM system outputs to preserve the privacy of event logs while maintaining effective threat detection capabilities. It rigorously characterizes the pivotal role of the privacy budget ε in governing the trade-off between privacy guarantees and analytical utility. Through evaluation on real-world systems, the research demonstrates that the proposed approach enables meaningful collaborative threat analysis even under stringent privacy constraints, thereby establishing differential privacy as a foundational mechanism for privacy-preserving threat intelligence sharing.
📝 Abstract
To resolve the acute problem of privacy protection and guarantee that data can be used in the context of threat intelligence, this paper considers the implementation of Differential Privacy (DP) in cybersecurity analytics. DP, which is a sound mathematical framework, ensures privacy by adding a controlled noise to data outputs and thus avoids sensitive information disclosure even with auxiliary datasets. The use of DP in Security Information and Event Management (SIEM) systems is highlighted, and it can be seen that DP has the capability to protect event log and threat data analysis without interfering with the analytical efficiency. The utility versus privacy trade-offs linked to the maximization of the epsilon parameter, which is one of the critical components of DP mechanisms, is pointed out. The article shows the transformative power of DP in promoting safe sharing of data and joint threat intelligence through real-world systems and case studies. Finally, this paper makes DP one of the key strategies to improve privacy-preserving analytics in the field of cybersecurity.