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A Study on k-anonymity, l-diversity, and t-closeness Techniques focusing Medical Data


Keerthana Rajendran, Manoj Jayabalan, Muhammad Ehsan Rana


Vol. 17  No. 12  pp. 172-177


In today’s world, most organizations are facing data accumulation in massive amounts and storing them in large databases. Myriad of them, the particular healthcare industry has recognized the potential use of these data to make informed decisions. Data from the Electronic Health Records (EHRs) system are prone to privacy violations, especially when stored in healthcare medical servers. Privacy Preserving Data Publishing (PPDP) caters means to publish useful information while preserving data privacy by employing assorted anonymization methods. This paper provides a discussion on several anonymity techniques designed for preserving the privacy of microdata. This research aims to highlight three of the prominent anonymization techniques used in medical field, namely k-anonymity, l-diversity, and t-closeness. The benefits and limitations of these techniques are also reviewed.


PPDP data anonymization k-anonymity l-diversity t-closeness