Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910039446388736 |
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| author | Niu, Haoran Barber, K. Suzanne |
| author_facet | Niu, Haoran Barber, K. Suzanne |
| contents | 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 such 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., 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 addresses the core question: Can the disclosure of a given identity attribute possibly lead to the disclosure of another attribute? The code for the privacy risk prediction framework is available at: https://github.com/niu-haoran/Privacy-Risk-Predictions-and-UTCID-Identity-Ecosystem.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape Niu, Haoran Barber, K. Suzanne Machine Learning Cryptography and Security Social and Information Networks 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 such 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., 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 addresses the core question: Can the disclosure of a given identity attribute possibly lead to the disclosure of another attribute? The code for the privacy risk prediction framework is available at: https://github.com/niu-haoran/Privacy-Risk-Predictions-and-UTCID-Identity-Ecosystem.git. |
| title | Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape |
| topic | Machine Learning Cryptography and Security Social and Information Networks |
| url | https://arxiv.org/abs/2508.04542 |