ArticleBMC medical informatics and decision making2019
A privacy-preserving distributed filtering framework for NLP artifacts.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- De-identification of free text data containing personal health information: a scoping review of reviews.International journal of population data science · 2023Article
- A flexible and stretchable bionic true random number generator.Nano research · 2022Article
- Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective.Automated software engineering · 2021Article
- Resilience of clinical text de-identified with "hiding in plain sight" to hostile reidentification attacks by human readers.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Privacy-Preserving Deep Learning NLP Models for Cancer Registries.IEEE transactions on emerging topics in computingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMedical data sharing is a big challenge in biomedicine, which often hinders collaborative research. Due to privacy concerns, clinical notes cannot be directly shared. A lot of efforts have been dedicated to de-identifying clinical notes but it is still very challenging to accurately locate and scrub all sensitive elements from notes in an automatic manner. An alternative approach is to remove sentences that might contain sensitive terms related to personal information.
methodsA previous study introduced a frequency-based filtering approach that removes sentences containing low frequency bigrams to improve the privacy protection without significantly decreasing the utility. Our work extends this method to consider clinical notes from distributed sources with security and privacy considerations. We developed a novel secure protocol based on private set intersection and secure thresholding to identify uncommon and low-frequency terms, which can be used to guide sentence filtering.
resultsAs the computational cost of our proposed framework mostly depends on the cardinality of the intersection of the sets and the number of data owners, we evaluated the framework in terms of these two factors. Experimental results demonstrate that our proposed method is scalable in various experimental settings. In addition, we evaluated our framework in terms of data utility. This evaluation shows that the proposed method is able to retain enough information for data analysis.
conclusionThis work demonstrates the feasibility of using homomorphic encryption to develop a secure and efficient multi-party protocol.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.