How to Understand Data Sensitivity? A Systematic Review by Comparing Four Domains

Shiyuan Cheng, Jie Zhang, Yuji Dong*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


Based on all the IT services provided in our daily life, massive data are created and collected. A lot of collected data are sensitive because of the privacy and security concerns. For the sensitive data processing, the service providers need to pay extra attention to avoid any serious consequences. Because the security techniques always have a price, there is a trade-off between the security level and budget. In some domains like banking and medicine, some guidelines have been designed for sensitive data processing. However, the provided services nowadays are more and more complicated and usually cross different domains. How to understand data sensitivity in a comprehensive, perspective and therefore design the better information processing systems becomes a significant issue. This paper tries to understand the data sensitivity by comparing the data sensitivity grading frameworks in four domains: Banking, Communication, Medicine and Education. Their perspectives on data sensitivity is similar on some parts. However, for some types of the data, the sensitivity level can be different from different domain perspectives. The findings and analysis give our insight on data sensitivity.

Original languageEnglish
Title of host publicationBDE 2022 - 2022 4th International Conference on Big Data Engineering
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450395632
Publication statusPublished - 26 May 2022
Event4th International Conference on Big Data Engineering, BDE 2022 - Virtual, Online, China
Duration: 26 May 202228 May 2022

Publication series

NameACM International Conference Proceeding Series


Conference4th International Conference on Big Data Engineering, BDE 2022
CityVirtual, Online


  • Cross-domain
  • Data Sensitivity
  • Privacy
  • Security Policy


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