Preserving Privacy of Data Using K-Anonymisation and T-Closeness
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 3
Abstract
In recent years, the growth of Electronic Health Record (EHR) technology has increased the amount of clinical data being electronically available. Medical data, scientific documents, and also digitalised patient health records, are important sources for clinical research. The cause for the leakage of private data seems to be the traditional data mining techniques and algorithms which operates on the original data set itself. Privacy preservation for individual’s medical information is vital before taking it to the secondary stages of research. To overcome these challenges imposed by traditional data mining techniques, privacy-preserving data mining (PPDM) has become one of the newest trends in privacy and security in data mining research. Modification of the data needs algorithms to be developed which, however should not compromise the privacy of the original data. This stands as the main aim of privacy preservation. Different techniques such as K anonymity, L-Diversity and T-closeness are used to preserve privacy of sensitive data.
Authors and Affiliations
Anu Rinny Sunny
Keywords
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- EP ID EP19879
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