Work

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

Year: 2025

Type: article

Abstract: 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 of 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 anevolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131239

Authors: Sancho Amaral Simões, João P. Vilela, Miriam Seoane Santos, Pedro Henriques Abreu

Institutions: University of Coimbra, Universidade do Porto

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funder: Fundação para a Ciência e a Tecnologia