🤖 AI Summary
Amid escalating privacy breaches and evolving global regulatory landscapes, this study addresses the lack of a unified conceptual framework for privacy breach analysis. Through a systematic literature review (SLR) covering 2010–2024, we synthesize privacy breach classification research using keyword frequency analysis and domain-driven design. Our primary contribution is the first comprehensive privacy breach taxonomy, spanning seven interrelated dimensions: breach classification, detection, prediction, risk analysis, data types, attack vectors, and defense mechanisms. Results reveal that classification and detection remain dominant research foci, whereas predictive modeling, location privacy, and healthcare-related breaches represent significant underexplored areas. The taxonomy clarifies the field’s knowledge structure, precisely identifies theoretical frontiers and practical blind spots, and provides a rigorous, extensible foundation for advancing both foundational research and real-world privacy engineering solutions.
📝 Abstract
In response to the rising frequency and complexity of data breaches and evolving global privacy regulations, this study presents a comprehensive examination of academic literature on the classification of privacy breaches and violations between 2010-2024. Through a systematic literature review, a corpus of screened studies was assembled and analyzed to identify primary research themes, emerging trends, and gaps in the field. A novel taxonomy is introduced to guide efforts by categorizing research efforts into seven domains: breach classification, report classification, breach detection, threat detection, breach prediction, risk analysis, and threat classification. An analysis reveals that breach classification and detection dominate the literature, while breach prediction and risk analysis have only recently emerged in the literature, suggesting opportunities for potential research impacts. Keyword and phrase frequency analysis reveal potentially underexplored areas, including location privacy, prediction models, and healthcare data breaches.