Personalized Publishing of Data with Multiple Sensitive Attributes based on Sensitivity Level
Ram Prasad Reddy Sadi, Panduranga Vital Terlapu · Solid State Technology · 2020
Data publishing scenario without compromising privacy and utility is challenging and essential forindividuals, researchers and data providers. Much of the research work in this direction assumes that eachindividual is associated with only one record in a dataset and has only sensitive attribute, which really isnot realistic in real world scenarios. If the person possessing multiple sensitive attributes appears morethan once in the dataset, several privacy breaches might take place. The practical scenarios in privacypreserving data publishing with each individual appearing multiple times and each occurrence havingmultiple sensitive attributes has not attracted much attention of researchers. We call such datasets as(1:M:N)-datasets. This paper attempts to provide a new privacy model, (k,l,s)-covering model that tends toexposure chances in (1:M:N)-dataset distributing.This paper also includes personalization where a userhas the privilege to specify whether to disclose the data or not. We also present an effective generalizationalgorithm, (1:M:N)-generalization, as part of the model, to retain privacy and the same time provide utilityfor the published data. The model is tested on real world datasets and the results showed excellentimprovement with respect to utility of the data and execution time when compared to other existingapproaches