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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)

Software:

UCI-ml; gmp; HElib
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