QIDLEARNINGLIB: A Python library for quasi-identifier recognition and evaluation

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Simoes, SA (Author) Other; João P. Vilela (Author) FCUP; Santos, MS (Author) FCUP; Abreu, PH (Author) Other

Journal

Title: Neurocomputing

Vol. 654

ISSN: 0925-2312

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-019-XJZ

DOI: 10.1016/j.neucom.2025.131239

Abstract (EN): Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15