Performance Interpretation of k-Anonymization Algorithms for Discernibility Metric
DOI:
https://doi.org/10.26438/ijcse/v5i11.7478Keywords:
Metrics, Discernibility Metric(DM), Equivalence Class, Privacy Preserving Data Publishing (PPDP), Quasi identifier (QID), American Time Use Survey (ATUS)Abstract
Advancement in technology and web based activities has increased the size of data sets which may cause the risk of re-identification about individual’s information. Multifarious techniques have been suggested for anonymizing the data sets. Aforesaid techniques ensure the individual’s identity to remain anonymous. As a result of that, privacy preservation in the field of data publishing has become an active area for research. In this paper an evaluation of various k-anonymity algorithms has been carried out with the objective of identifying the value of discernibility that occurs due to anonymization. An experiment has been performed to determine the value of discernibility based on the type of attribute(s) on three publically available data sets that carries different dimensions.
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