Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study

Journal of Medical Internet Research
Mohamed AbdallaFrank Rudzicz

Abstract

Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. This paper aims to demonstrate that traditional word embeddings created on clinical corpora that have been deidentified by removing personal health information (PHI) can nonetheless be exploited to reveal sensitive patient information. We used embeddings created from 400,000 doctor-written consultation notes and experimented with 3 common word embedding methods to explore the privacy-preserving properties of each. We found that if publicly released embeddings are trained from a corpus anonymized by PHI removal, it is possible to reconstruct up to 68.5% (n=411/600) of the full names that remain in the deidentified corpus and associated sensitive information to specific patients in the corpus from which the embeddings were created. We also found that the distance between the word vector representation of a patient's name and a diagnostic billing code is informative and differs significantly from the distance between the name and a code not billed for that patient. Special c...Continue Reading

References

Jul 26, 2008·BMC Medical Informatics and Decision Making·Ishna NeamatullahGari D Clifford
May 25, 2016·Scientific Data·Alistair E W JohnsonRoger G Mark
Jan 4, 2017·Journal of the American Medical Informatics Association : JAMIA·Franck DernoncourtPeter Szolovits
Sep 16, 2018·Journal of Biomedical Informatics·Yanshan WangHongfang Liu
Jul 3, 2019·Journal of the American Medical Informatics Association : JAMIA·Yuqi SiKirk Roberts

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