Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

Abstract

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…Kabou et al [108] presented a comprehensive survey about the PPDDP, and summarized different studies used for the PPDDP. Shi et al [109] presented a method for PPDDP using distance and information entropy concepts. The proposed method ensures that individual privacy is preserved after the data has been subjected to multiple releases.…”

Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

“…Microaggregation is a perturbative data protection method (Shi et al 2018), in which small clusters in a dataset can be replaced each original record by the centred of the corresponding cluster (each cluster should have between k and 2k elements), the larger the k, the larger the information loss and the lower the disclosure risk. It ensures k-anonymity only when multivariate microaggregation is applied processing all the variables of the dataset (Mahawaga Arachchige et al 2020;Du et al 2020).…”

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

“…For preserving the privacy, the algorithms in privacy models practice different approaches. ese approaches can be categorized to (i) generalization [1][2][3][4][5][13][14][15] (i.e., greedily convert the more specialized values to less specialized values), (ii) anatomy [25,26] (i.e., partition the QI and S attributes), and (iii) microaggregation [29,30] (i.e., dataset is partitioned into clusters where QI values of records are replaced with the mean of value). e proposed work in this paper considers the syntactic data privacy, using generalization and anatomy for MSAs.…”