Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape
This work addresses privacy risk prediction for individuals and organizations by providing a foundational model, though it appears incremental as it builds on existing graph theory and neural network methods.
The research tackled the problem of predicting privacy risks by analyzing over 5,000 identity theft and fraud cases to identify exposed personal data types, exposure frequencies, and consequences, resulting in a graph-based model and framework that effectively estimates the likelihood of further disclosures when certain attributes are compromised.
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?