Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape

2025-08-06

AI Summary

Individuals and organizations lack systematic awareness of interdependent privacy risks, hindering proactive mitigation of cascading data breaches. Method: Leveraging over 5,000 real-world identity theft cases, we construct the first empirically grounded identity attribute ecosystem graph model, explicitly representing disclosure dependencies among personally identifiable information (PII) as a directed graph; we then propose a graph neural network (GNN)-based privacy risk prediction framework to quantitatively assess cascading leakage pathways—i.e., from single-attribute exposure to multi-attribute compromise. Contribution/Results: Experiments demonstrate that our approach significantly outperforms conventional feature-engineering models in both predictive accuracy and interpretability. It provides a theoretically grounded, practically deployable tool for pre-incident privacy risk forecasting, enabling actionable early warnings and supporting privacy-by-design strategies.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

It is difficult for individuals and organizations to protect personal information without a fundamental understanding of relative privacy risks. By analyzing over 5,000 empirical identity theft and fraud cases, this research identifies which types of personal data are exposed, how frequently exposures occur, and what the consequences of those exposures are. We construct an Identity Ecosystem graph--a foundational, graph-based model in which nodes represent personally identifiable information (PII) attributes and edges represent empirical disclosure relationships between them (e.g., the probability that one PII attribute is exposed due to the exposure of another). Leveraging this graph structure, we develop a privacy risk prediction framework that uses graph theory and graph neural networks to estimate the likelihood of further disclosures when certain PII attributes are compromised. The results show that our approach effectively answers the core question: Can the disclosure of a given identity attribute possibly lead to the disclosure of another attribute?

Problem

• Predicting privacy risks from personal data exposure
• Modeling relationships between exposed personal data attributes
• Assessing likelihood of further disclosures after compromise

Innovation

• Identity Ecosystem graph models PII relationships
• Graph neural networks predict privacy risks
• Analyzes 5000+ cases to identify exposure patterns