Private-Key Fully Homomorphic Encryption for Private Classification

被引：4

作者：

Wood, Alexander ^{[1
,2
,3
,4
]}

Shpilrain, Vladimir ^{[5
,6
]}

Najarian, Kayvan ^{[2
,3
,4
]}

Mostashari, Ali ^{[7
]}

Kahrobaei, Delaram ^{[1
,8
]}

机构：

[1] CUNY, Grad Ctr, Dept Comp Sci, New York, NY 10016 USA

[2] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA

[3] Univ Michigan, Ctr Integrat Res Crit Care, Ann Arbor, MI 48109 USA

[4] Univ Michigan, Emergency Med Dept, Ann Arbor, MI 48109 USA

[5] CUNY, Grad Ctr, Dept Math, New York, NY USA

[6] CUNY City Coll, Dept Math, New York, NY USA

[7] LifeNome Inc, New York, NY USA

[8] NYU, Tandon Sch Engn, Dept Comp Sci, New York, NY USA

来源：

MATHEMATICAL SOFTWARE - ICMS 2018 | 2018年 / 10931卷

关键词：

Fully homomorphic encryption; Data privacy; Machine learning;

D O I：

10.1007/978-3-319-96418-8_56

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：475 / 481

页数：7

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