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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Simoes, SA (Autor) Outra; João P. Vilela (Autor) FCUP; Santos, MS (Autor) FCUP; Abreu, PH (Autor) Outra

Revista

Título: Neurocomputing

Vol. 654

ISSN: 0925-2312

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15