Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Abstract

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

Improved angelization technique against background knowledge attack for 1:M microdata

“…For future work considerations, the proposed algorithm can be extended to implement privacy in a dynamic data publishing scenario ( Xiao & Tao, 2007 ; Khan et al., 2020b ) for periodic or non-periodic updates. Similarly, the proposed work can be extended to a cluster based anonymization technique to more efficiently overcome the problem of privacy and utility paradigm ( Safi & Hwang, 2022 ). Another privacy extension can be privacy-preserving federated learning (PPFL) ( Yin, Zhu & Hu, 2021 ).…”

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“…In addition to the analysis given in Table 4, further information about clusteringbased anonymization can be gained from previous surveys centering solely on these techniques [111][112][113]. Recently, clustering-based anonymization methods have gained popularity from multiple perspectives [114].…”

Guardians of Privacy: Understanding the European Union's Framework for Biometric Data Protection

“…Through a variety of techniques applied to data, attributes, and/or data structures, identification is rendered impossible by the removal or alteration of data. This kind of process results in an anonymized dataset, which doesn't contain any personal data that can be linked back to an individual [8]. Anonymization effectively mitigates GDPR restrictions, as the data no longer contains information pertaining to identifiable individuals.…”