Open Access

ARTICLE

A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

Burak Cem Kara^1,2,*, Can Eyüpoğlu¹

1 Department of Computer Engineering, Turkish Air Force Academy, National Defence University, Yeşilyurt,Istanbul, Turkey
2 Department of Computer Engineering, Atatürk Strategic Studies and Graduate Institute, National Defence University, Beşiktaş, Istanbul, Turkey

* Corresponding Author: Burak Cem Kara. Email:

Computers, Materials & Continua 2023, 76(2), 1515-1535. https://doi.org/10.32604/cmc.2023.040274

Received 12 March 2023; Accepted 15 June 2023; Issue published 30 August 2023

Abstract

Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian algorithm. The connectivity-based outlier factor (COF) algorithm is used to detect outliers. Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data. COF, on the other hand, is used to discover outliers in high-dimensional datasets with complicated structures. The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on k-anonymization. In addition, it outperforms other algorithms in the discernibility metric (DM), normalized average equivalence class size (Cavg), global certainty penalty (GCP), query error rate, classification accuracy (CA), and F-measure metrics. Moreover, the increase in the values of the GCP and error rate metrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.

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Keywords

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