PERFORMANCE EXPLANATION OF K-ANONYMIZATION ALGORITHMS FOR AVERAGE CLASS PARTITIONING METRIC
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Abstract
Nowadays besides the excessive use of technology peoples are unwilling to store their information because of privacy threat. In various domains data collection plays a significant role and beneficial in various fields such as Health care/Medical field etc. Some time collected data contains such sensitive data which is quiet personal and need not to be disclosed but if some of the information is revealed it may causes major risk. Privacy Protection data publishing (PPDP) works with an aim to give protection to an individual against identification risk and uses the process of data sanitization before publishing. Various techniques ensures the individuals identity to remain anonymous .In this research paper assessment of various k-anonymity algorithms have been made by keeping an objective in mind to determine that how well the equivalence classes have been formed when anonymization have been performed and data set is divided in to various classes. Moreover, proper investigation have been conducted in the direction of identifying the value of average equivalence class size on three publically available data sets with varying dimensions.
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