Wood, Alexander; Shpilrain, Vladimir; Najarian, Kayvan; Mostashari, Ali; Kahrobaei, Delaram Private-key fully homomorphic encryption for private classification. (English) Zbl 1395.68115 Davenport, James H. (ed.) et al., Mathematical software – ICMS 2018. 6th international conference, South Bend, IN, USA, July 24–27, 2018. Proceedings. Cham: Springer (ISBN 978-3-319-96417-1/pbk; 978-3-319-96418-8/ebook). Lecture Notes in Computer Science 10931, 475-481 (2018). Summary: 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.For the entire collection see [Zbl 1391.68004]. MSC: 68P25 Data encryption (aspects in computer science) 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62P10 Applications of statistics to biology and medical sciences; meta analysis 68T05 Learning and adaptive systems in artificial intelligence 92C50 Medical applications (general) Keywords:fully homomorphic encryption; data privacy; machine learning Software:UCI-ml; gmp; HElib PDFBibTeX XMLCite \textit{A. Wood} et al., Lect. Notes Comput. Sci. 10931, 475--481 (2018; Zbl 1395.68115) Full Text: DOI