Private naive bayes classification of personal biomedical data: Application in cancer data analysis. (February 2019)

• Record Type: Journal Article
• Authors: Wood, Alexander; Shpilrain, Vladimir; Najarian, Kayvan; Kahrobaei, Delaram
• Is Part Of: Computers in biology and medicine. Volume 105(2019)
• Page Start: 144
• Page End: 150
• Publication Date: 2019-02
• Subjects: cryptographic protocols -- Data privacy -- Fully homomorphic encryption -- Medical information systems -- Predictive models; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.11.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients' privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when in use by outside parties. Fully homomorphic encryption (FHE) enables computation over encrypted medical data while ensuring data privacy. In this paper we use private-key fully homomorphic encryption to design a cryptographic protocol for private Naive Bayes classification. This protocol allows a data owner to privately classify his or her information without direct access to the learned model. We apply this protocol to the task of privacy-preserving classification of breast cancer data as benign or malignant. Our results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification.