Uniqueness Ratio as a Predictor of a Privacy Leakage

20260 citationsJournal Articlegreen Open Access

Authors

ALSalem AlKhashti, Danah · Lourdes University

Abstract

This paper introduces Uniqueness Ratio as a pre-join metric for predicting privacy leakage in integrated databases. It analyzes how attribute combinations become identifying only after dataset joins, enabling early risk assessment before data integration using machine learning.

Topics & Keywords

Information and Cyber Security Privacy-Preserving Technologies in Data Network Security and Intrusion Detection

Publication Details

Published in: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17916479

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Uniqueness Ratio as a Predictor of a Privacy Leakage

Authors

Abstract

Topics & Keywords

Publication Details