TY - GEN
T1 - How to Understand Data Sensitivity? A Systematic Review by Comparing Four Domains
AU - Cheng, Shiyuan
AU - Zhang, Jie
AU - Dong, Yuji
N1 - Funding Information:
This work was partially supported by the National Natural Science Foundation of China under Grant No. 62002296; the Natural Science Foundation of Jiangsu Province under Grant No. BK20200250; the XJTLU Key Programme Special Fund under Grant No. KSF-E-54 and the XJTLU Postgraduate Research Scholarship Scheme under Grant No. FOSA21120015.
Publisher Copyright:
© 2022 ACM.
PY - 2022/5/26
Y1 - 2022/5/26
N2 - 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.
AB - 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.
KW - Cross-domain
KW - Data Sensitivity
KW - Privacy
KW - Security Policy
UR - http://www.scopus.com/inward/record.url?scp=85135055273&partnerID=8YFLogxK
U2 - 10.1145/3538950.3538953
DO - 10.1145/3538950.3538953
M3 - Conference Proceeding
AN - SCOPUS:85135055273
T3 - ACM International Conference Proceeding Series
SP - 13
EP - 20
BT - BDE 2022 - 2022 4th International Conference on Big Data Engineering
PB - Association for Computing Machinery
T2 - 4th International Conference on Big Data Engineering, BDE 2022
Y2 - 26 May 2022 through 28 May 2022
ER -