Protecting privacy in the age of big data: exploring data linking methods for quasi-identifier selection

Abstract This paper explores the limitations faced by current solutions for selecting quasi-identifying attributes in the context of Privacy-Preserving Data Publishing (PPDP). PPDP stipulates that any published personal data should not be linkable to other available data sources in a manner that could potentially lead to individual re-identification or compromise sensitive data. The state-of-the-art methods for selecting quasi-identifying attributes commonly rely on heuristic evaluations to assess the risk of re-identification associated with each attribute. We hypothesize that these heuristic-based methods could be significantly improved by complementing them with empirical methods capable of quantifying the external linkability of dataset attributes. This empirical layer would enable a fine-tuning of the obfuscation of attributes within the dataset, thereby preventing the unnecessary privatization of attributes beyond potential attackers’ reach while ensuring privatization of those easily accessible. For this purpose, we explore recent advancements in identifying semantically related datasets across heterogeneous data sources. Although initially developed for purposes beyond privacy preservation, these methods support our initiative by uncovering potential links with external data and thus providing empirical evidence for the identification of attributes as quasi-identifiers. Finally, we discuss potential pathways to implement this empirical layer in quasi-identifier identification systems.