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.

翻译：为解决隐私保护的紧迫问题并确保数据在威胁情报背景下可用，本文探讨了差分隐私在网络安全分析中的实施。差分隐私作为一种严谨的数学框架，通过向数据输出添加受控噪声来确保隐私，从而即使存在辅助数据集也能避免敏感信息泄露。本文重点分析了差分隐私在安全信息与事件管理系统中的应用，证明其能够在保障事件日志与威胁数据分析的同时维持分析效能。研究指出与ε参数最大化相关的效用-隐私权衡问题，该参数是差分隐私机制的核心要素之一。通过实际系统与案例研究，本文展示了差分隐私在促进安全数据共享与协同威胁情报方面的变革性潜力。最终，本文将差分隐私定位为提升网络安全领域隐私保护分析能力的关键策略之一。