Visible to the public Research on K Anonymity Algorithm Based on Association Analysis of Data Utility

TitleResearch on K Anonymity Algorithm Based on Association Analysis of Data Utility
Publication TypeConference Paper
Year of Publication2017
AuthorsGao, Y., Luo, T., Li, J., Wang, C.
Conference Name2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
KeywordsAlgorithm design and analysis, anonymity, association analysis, classification evaluation performance, composability, Correlation, correlation coefficient, Data analysis, data privacy, data utility, feature maintenance, Grey relational analysis K anonymous algorithm, grey systems, Human Behavior, information loss, K anonymity, K anonymity algorithm, medical data privacy, medical information systems, Metrics, Optimization, PCA-GRA K anonymous algorithm, personal identity, personal privacy information, principal component analysis, Privacy preserve, pubcrawl, Publishing, resilience, Resiliency

More and more medical data are shared, which leads to disclosure of personal privacy information. Therefore, the construction of medical data privacy preserving publishing model is of great value: not only to make a non-correspondence between the released information and personal identity, but also to maintain the data utility after anonymity. However, there is an inherent contradiction between the anonymity and the data utility. In this paper, a Principal Component Analysis-Grey Relational Analysis (PCA-GRA) K anonymous algorithm is proposed to improve the data utility effectively under the premise of anonymity, in which the association between quasi-identifiers and the sensitive information is reckoned as a criterion to control the generalization hierarchy. Compared with the previous anonymity algorithms, results show that the proposed PCA-GRA K anonymous algorithm has achieved significant improvement in data utility from three aspects, namely information loss, feature maintenance and classification evaluation performance.

Citation Keygao_research_2017