Biomedical & Clinical Informatics Lab

Department of Computational Medicine & Bioinformatics

Private-Key Fully Homomorphic Encryption for Private Classification

TitlePrivate-Key Fully Homomorphic Encryption for Private Classification
Publication TypeConference Paper
Year of Publication2018
AuthorsWood A, Shpilrain V, Najarian K, Mostashari A, Kahrobaei D
EditorDavenport JH, Kauers M, Labahn G, Urban J
Conference NameMathematical Software – ICMS 2018
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-319-96418-8
Abstract

Fully homomophic encryption enables private computation over sensitive data, such as medical data, via potentially quantum-safe primitives. In this extended abstract we provide an overview of an implementation of a private-key fully homomorphic encryption scheme in a protocol for private Naive Bayes classification. This protocol allows a data owner to privately classify her data point without direct access to the learned model. We implement this protocol by performing privacy-preserving classification of breast cancer data as benign or malignant.