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

共 27 条

[1] Alperin-Sheriff J, 2014, LECT NOTES COMPUT SC, V8616, P297, DOI 10.1007/978-3-662-44371-2_17
[2] Privacy-Preserving Processing of Raw Genomic Data
Ayday, Erman
Raisaro, Jean Louis
Hengartner, Urs
Molyneaux, Adam
Hubaux, Jean-Pierre
[J]. DATA PRIVACY MANAGEMENT AND AUTONOMOUS SPONTANEOUS SECURITY, DPM 2013, 2014, 8247 : 133 - 147
[3] Bos Joppe W., 2013, Cryptography and Coding. 14th IMA International Conference, IMACC 2013. Proceedings: LNCS 8308, P45, DOI 10.1007/978-3-642-45239-0_4
[4] Machine Learning Classification over Encrypted Data
Bost, Raphael
Popa, Raluca Ada
Tu, Stephen
Goldwasser, Shafi
[J]. 22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015), 2015,
[5] Brakerski Zvika, 2014, ACM Transactions on Computation Theory, V6, DOI 10.1145/2633600
[6] Brakerski Z, 2011, LECT NOTES COMPUT SC, V6841, P505, DOI 10.1007/978-3-642-22792-9_29
[7] Efficient Fully Homomorphic Encryption from (Standard) LWE
Brakerski, Zvika
Vaikuntanathan, Vinod
[J]. 2011 IEEE 52ND ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS 2011), 2011, : 97 - 106
[8] Bruekers F., 2008, TECHNICAL REPORT
[9] Homomorphic Computation of Edit Distance
Cheon, Jung Hee
Kim, Miran
Lauter, Kristin
[J]. FINANCIAL CRYPTOGRAPHY AND DATA SECURITY (FC 2015), 2015, 8976 : 194 - 212
[10] Dowlin N, 2016, PR MACH LEARN RES, V48

← 1 2 3 →