1.Nsfocus Information Technology Co.，Ltd.，Beijing 100089，China； 2.Department of Automation，Tsinghua University，Beijing 100084，China
Abstract： As a privacy protection technology, de-identification has been widely used in data publishing scenarios. However, in the era of big data, attackers may obtain more associated data, and there is still a risk of re-identification attacks on de-identified datasets. Based on information entropy and information security risk assessment framework, this paper proposes a comprehensive re-identification risk assessment method. Firstly, the various attribute combinations of a de-identified dataset that attackers may utilize are summarized into several vulnerabilities, and then these vulnerabilities are evaluated one by one from probability and impact dimension. Finally, in order to comprehensively evaluate the re-identification risk of the dataset, this paper constructs a fast evaluation algorithm based on entropy increments and weights. Extensive experimental results demonstrate that the proposed evaluation method can comprehensively and intuitively reflect the risk distribution and trend.
Key words : privacy protection；de-identified datasets；re-identification risk assessment；information entropy
