Abstract
This article presents a two-step approach focusing on the identification of sensitive data within documents. The proposed pipeline first detects the domain of a document, then identifies the sensitive information it contains. Detection of domains allows to better understand the context of a documents, hence supports the disambiguation of potentially sensitive information. The prototype considers three domains: health, business and “other”. The system developed for the domain detection step is built and evaluated on a corpus composed of clinical notes, and articles about business or art from Forbes, Reuters, and The New York Times. The identification of sensitive information relies on a Conditional Random Fields (CRF) model.
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Acknowledgment
As part of this work, the Deidentified Clinical Records used were provided by the i2b2 National Center for Biomedical Computing funded by U54LM008748 and were originally prepared for the Shared Tasks for Challenges in NLP for Clinical Data organized by Dr. Ozlem Uzuner, i2b2 and SUNY.
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Briand, A., Zacharie, S., Jean-Louis, L., Meurs, MJ. (2018). Identification of Sensitive Content in Data Repositories to Support Personal Information Protection. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_86
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