Data anonymization: a novel optimal k-anonymity algorithm for identical generalization hierarchy data in IoT

Abstract

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Advancement in the Internet of Things (IoT) technologies makes life more convenient for people. Data sensed from the devices can be used for analyzing and responding to people’s needs seamlessly. An important consequence of such convenience is that privacy protection becomes a very important issue to be addressed effectively. Various data anonymization model has been proposed for such issue—one of the most widely applied models is the k-anonymity. The k-anonymity prevents the re-identification by replacing the input data with its more general form for transforming the data to have at least k identical tuples. In this paper, we focus on a special case of the input datasets which all the quasi-identifiers, the linkable attributes in the dataset, have identical data types, so-called identical generalization hierarchy (IGH). The solutions for such case will be applicable effectively to address the general IoT data privacy protection due to its data nature. We proposed a novel method to provide a globally optimized k-anonymity solution for the IGH datasets. The proposed algorithms determine an optimal solution based on the characteristics of the IGH data by visiting and evaluating only essential nodes of generalization lattice that satisfy the k-anonymity. Since the k-anonymization problem is an NP-hard, we show that our algorithm can efficiently find an optimal k-anonymity solutions with exploiting such special characteristics of the IGH data, i.e., the optimality between the nodes in different levels of generalization lattice. From the experimental results, it is obvious that our algorithm is much more efficient than the comparative algorithms by less searching on the given lattice.