Scholarlink

<p>Although privacy protection is increasingly important in the digital age, current measures predominantly focus on safeguarding high-sensitivity data, often overlooking the risks associated with low-sensitivity data. Here, we utilize low-sensitivity anonymous interaction data, which depict the behavioral interaction patterns between users and their contacts, to construct multidimensional social signature for identifying users in anonymous datasets. We investigate the potential of low-sensitivity social signature for user identification and propose a classification framework for measuring feature sensitivity levels. Among the test datasets, the accuracy of user identification can reach up to 87<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_16663_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation> in email communication, and remains wide applicability across different datasets. This suggests that even anonymous low-sensitivity interaction data should be considered personal data warranting protection under existing data protection regulations. We challenge the efficacy of current data anonymization methods and offer new perspectives on low-sensitivity data privacy protection through our feature sensitivity classification framework.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multidimensional social signature de-anonymizes low-sensitivity data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

查看全文
  • Weiwen Jia,
  • Bin Zhou,
  • Xin Lu,
  • Xiaoke Xu

摘要

Although privacy protection is increasingly important in the digital age, current measures predominantly focus on safeguarding high-sensitivity data, often overlooking the risks associated with low-sensitivity data. Here, we utilize low-sensitivity anonymous interaction data, which depict the behavioral interaction patterns between users and their contacts, to construct multidimensional social signature for identifying users in anonymous datasets. We investigate the potential of low-sensitivity social signature for user identification and propose a classification framework for measuring feature sensitivity levels. Among the test datasets, the accuracy of user identification can reach up to 87 \(\%\) in email communication, and remains wide applicability across different datasets. This suggests that even anonymous low-sensitivity interaction data should be considered personal data warranting protection under existing data protection regulations. We challenge the efficacy of current data anonymization methods and offer new perspectives on low-sensitivity data privacy protection through our feature sensitivity classification framework.