Improved slicing algorithm for greater utility in privacy preserving data publishing
Ajinkya A Dhaigude, Preetham Kumar · 2014
Several algorithms and techniques have been proposed in recent years for the publication of sensitive microdata. However, there is a trade-off to be considered between the level of privacy offered and the usefulness of the published data. Recently, slicing was proposed as a novel technique for increasing the utility of an anonymized published dataset by partitioning the dataset vertically and horizontally. This work proposes a novel technique to increase the utility of a sliced dataset even further by allowing overlapped clustering while maintaining the prevention of membership disclosure. It is further shown that using an alternative algorithm to Mondrian increases the efficiency of slicing. This paper shows though workload experiments that these improvements help preserve data utility better than traditional slicing.