Computer Science ›› 2018, Vol. 45 ›› Issue (4): 46-52.doi: 10.11896/j.issn.1002-137X.2018.04.006

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Survey on Application of Homomorphic Encryption in Encrypted Machine Learning

CUI Jian-jing, LONG Jun, MIN Er-xue, YU Yang and YIN Jian-ping   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Nowadays,the existing machine learning algorithms can not analyze and calculate the encrypted data,at the same time,many areas(such as medical industry,financial industry) strongly require data to keep private and secure while analyzed and calculated by untrusted person or company.All these lead to the generation and development of encrypted machine learning.Homomorphic encryption is the primary idea of solving this problem by ensuring that calculations on the cipher text without decrypting,which can result in the same result of the same calculations on the plain text.This paper conducted a survey on application of homomorphic encryption in encrypted machine learning.This work mainly introduced three kinds of algorithms(encrypted neural network,encrypted K-nearest Neighbor,encrypted decision tree and completely random forest) which are used to realize encrypted machine learning with homomorphic encryption,and also analyzed the scheme design from the aspects of correctness,security and efficiency.This paper summarized the construction of different encrypted machine learning algorithms,pointed out the key problems of homomorphic encryption for encrypted machine learning and the content that needs to be focused on in further studies,and provided some referen-ces for homomorphic encryption and encrypted machine learning.

Key words: Homomorphic encryption,Encrypted machine learning,Privacy preserving data mining

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