中圖分類號：TP309 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245390
中文引用格式： 齊俊，周小明，許超，等. 基于貝葉斯網絡的多方關聯數據訪問安全風險識別模型研究[J]. 電子技術應用，2024，50(9)：9-17.
英文引用格式： Qi Jun，Zhou Xiaoming，Xu Chao，et al. Research on multi-party associated data access security risk identification model based on Bayesian network[J]. Application of Electronic Technique，2024，50(9)：9-17.

Research on multi-party associated data access security risk identification model based on Bayesian network

Abstract： For the data center of modern power systems, identifying data inference risks during user access to data is particularly crucial. Especially when multiple users collude to steal data, it may lead to the inference of sensitive data from non sensitive data, resulting in sensitive data leakage and posing a serious threat to power dispatch and national security. Traditional access control mechanisms cannot identify this risk. Therefore, this article proposes a multi-party association data security risk identification model MPA-BN based on Bayesian networks, which comprehensively considers user access behavior, time patterns, interface types, and data interaction methods. Bayesian networks are used to analyze the access relationship between users and service interfaces, deeply explore the dependency relationship and probability characteristics between data, identify the correlation between external service interfaces in data, and potential risks of user combinations. The dataset used in this study is from the desensitization logs of the power company's data center, which includes 10 000 visiting users and generates approximately 1 million log entries. The experimental results show that the model can effectively identify the risk of multiple users colluding to steal sensitive data, providing stronger protection for the security of power system data.

Key words : data middle platform；data inference risk；Bayesian network；privacy protection