Privacy preservation in relational data through l-diversity and recursive (c, l) diversity anonymisation
by Saptarshi Chakraborty; B.K. Tripathy
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 7, No. 3/4, 2016

Abstract: Publication of huge amount of data generated by different organisations has catalysed the interest of scientific community to analyse and extract the hidden information in it. However, this leads to the serious problem of disclosing the sensitive information associated with the respondents. Over the years, many techniques have been developed in the form of k-anonymity and its improvements in the form of l-diversity to anonymise the data tables before publication. The number of distinct l-diversity algorithms proposed so far is very few in comparison to its counterpart k-anonymity although every algorithm achieving optimal k-anonymity by attribute generalisation can be extended to achieve distinct l-diversity. Also, to the best of our knowledge there is no concrete algorithm to deal with recursive (c, l) diversity. In this paper, we propose a new approach to achieve distinct l-diversity by adding fake tuples and extend it to propose a recursive (c, l) diversity algorithm. Through various experiments and measures, we established the efficiency of our proposed algorithm over the existing algorithms on l-diversity.

Online publication date: Mon, 30-Jan-2017

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