An effective value swapping method for privacy preserving data publishing

Abstract

Privacy is an important concern in the society, and it has been a fundamental issue when to analyze and publish data involving human individual's sensitive information. Recently, the slicing method has been popularly used for privacy preservation in data publishing, because of its potential for preserving more data utility than others such as the generalization and bucketization approaches. However, in this paper, we show that the slicing method has disclosure risks for some absolute facts, which would help the adversary to find invalid records in the sliced microdata table, resulting in breach of individual privacy. To increase the privacy of published data in the sliced tables, a new method called value swapping is proposed in this work, aimed at decreasing the attribute disclosure risk for the absolute facts and ensuring the l‐diverse slicing. By value swapping, the published table contains no invalid information such that the adversary cannot breach the individual privacy. Experimental results also show that the NEW method is able to keep more data utility than the existing slicing methods in a published microdata table. Copyright © 2016 John Wiley & Sons, Ltd.

A New Approach to Privacy-Preserving Multiple Independent Data Publishing

“…Random shuffling will break the association of the tuple but it will create some invalid records and, in some cases, it may increase the likelihood of privacy breach for a particular set of sensitive values [22]. For these special circumstances, we have introduced cell generalization to enhance the privacy of the equivalence class.…”

“…In the anonymization, column values are permuted randomly to break cross-column associations. There is a possibility of creating some invalid records [22] or incompatible tuples in the process. In line 2, tuple incompatibility is checked as in [22].…”

“…There is a possibility of creating some invalid records [22] or incompatible tuples in the process. In line 2, tuple incompatibility is checked as in [22]. If there are incompatible tuples, we generalize the particular cell values to satisfy k-anonymity.…”

“…Since there is no communication or information sharing between data owners in multiple independent data publications, collaborative privacy-preserving data publishing techniques [19][20][21] cannot be used to protect privacy in this case. In addition, we published earlier research on privacy-preserving data publishing [15,17,22]. To reduce the likelihood of composition attacks on published datasets, existing anonymization techniques [2,23,24] utilize generalization and perturbation, which decrease the data utility.…”

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

“…In relational databases, although identifiers are removed, a set of quasi-identifiers can re-identify a person in a published table [28]. To protect the re-identification of identity, k-anonymity [16] and l-diversity [17], among other methods, have been proposed to publish relational data with privacy preservation.…”