With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively reduced communication load.

随着网络技术和大规模计算技术的发展，分布式数据流已广泛应用于金融风险分析的应用中。但是，尽管数据挖掘揭示了财务模型，但它也越来越构成对隐私的威胁。因此，如何在有效的挖掘过程中防止隐私泄露对数据挖掘技术提出了新的挑战。本文主要针对当前金融数据挖掘中的隐私数据泄漏，结合现有的数据挖掘技术来研究数据挖掘和隐私保护。首先，定义了一种用于双重隐私保护的数据挖掘模型，该模型可以在满足隐私保护效果的同时更好地满足分布式数据流的特性。其次，提出了一种面向隐私的数据流挖掘算法，它使用随机干扰技术来有效保护原始敏感数据。最后，通过仿真实验对本文算法进行了分析和讨论，表明该算法可行，有效，可以更好地适应分布式数据流的分布和动态特性，同时达到较好的隐私保护效果，有效降低了通信负荷。