中圖分類號： TP399
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.12.001
引用格式： 陳磊，薛見新，張潤滋，等. 基于信息熵的數(shù)據(jù)集重標識風險評估方法[J].信息技術(shù)與網(wǎng)絡安全，2020，39(12)：1-7.

Re-identification risk assessment of de-identified datasets based on information entropy

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