.png){kind=icon}
.png){kind=icon}
Return
QIDLearningLib: A Python library for quasi-identifier recognition and evaluation
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2025.131239.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• QIDLearningLib is the first library for automated QID recognition in tabular datasets. • It integrates metrics from causality, data privacy, and data utility for flexible QID selection. • It provides metrics to evaluate the performance of the QID selection system, against a ground-truth. • Includes multiple optimization algorithms for QID selection based on user-defined metrics. • Supports redundancy analysis to identify the most relevant, non-overlapping metrics. • Provides graphical and testing tools for enhanced interpretability.
Keywords:
Python library
Quasi-identifiers
Data anonymization
Data privacy
Data utility
Causality
Optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.