|Title||Private-Key Fully Homomorphic Encryption for Private Classification|
|Publication Type||Conference Paper|
|Year of Publication||2018|
|Authors||Wood A, Shpilrain V, Najarian K, Mostashari A, Kahrobaei D|
|Editor||Davenport JH, Kauers M, Labahn G, Urban J|
|Conference Name||Mathematical Software – ICMS 2018|
|Publisher||Springer International Publishing|
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.