Automatic Extraction of Protected Health Information from Multilingual Hacker Communities

Cade Dacosta, Benjamin M. Ampel, Matthew Hashim, Hsinchun Chen
Last updated on Jun 9, 2026
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This research tackles the theft and resale of stolen health records discussed in hacker channels on encrypted platforms like Telegram and Discord. The authors build NERF-PHI, a named-entity-recognition system that, after machine-translating over three million multilingual hacker posts into English, automatically pulls out mentions of victims and medical data. They find that encoder-based language models are highly effective at surfacing this protected health information for investigators.

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APA Dacosta, C., Ampel, B. M., Hashim, M., & Chen, H. (2026). Automatic Extraction of Protected Health Information from Multilingual Hacker Communities. In Proceedings of the 59th Hawaii International Conference on System Sciences (HICSS). https://doi.org/10.24251/HICSS.2026.063
BibTeX
@inproceedings{dacosta2026automatic,
  title={Automatic Extraction of Protected Health Information from Multilingual Hacker Communities},
  author={Cade Dacosta and Benjamin M. Ampel and Matthew Hashim and Hsinchun Chen},
  booktitle={Proceedings of the 59th Hawaii International Conference on System Sciences (HICSS)},
  year={2026},
  doi={10.24251/HICSS.2026.063}
}
Protected Health Information Hacker Communities Named Entity Recognition Large Language Models Healthcare Cybersecurity
Benjamin M. Ampel

Benjamin M. Ampel

Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)

My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.