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

ABSTRACT:

Background

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.

Objective

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.

Methods

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.

Results

We found that if publicly released embeddings are train

SUBMITTER: Abdalla M 

PROVIDER: S-EPMC7391163 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature