| SPECIAL SECTION: TRUSTED COMPUTING AND INFORMATION SECURITY |
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Machine learning algorithm for a homomorphic encrypted data set |
| JIA Chunfu1,2, WANG Yafei1,2, CHEN Yang1, SUN Mengjie1, GE Fengyi1 |
1. College of Cyberspace Security, Nankai University, Tianjin 300350, China;
2. Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China |
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Abstract The continuous development of big data requires that data be stored and analyzed in the cloud, which leads to privacy leakage of sensitive data. This paper presents a machine learning classification algorithm for homomorphic encrypted data sets. Firstly, preprocess the data set to meet the requirements of homomofphic encryption. The encrypted data set is then sorted by protocol and classified. Finally, the classification results are obtained. The client can then upload encrypted data and ensure that the server will not get any sensitive information. A homomorphic encryption algorithm is used to ensure that the server can still perform required operations on the ciphertext. Tests show that this scheme can provide accurate, useful results with Bayes, hyperplane and decision tree classifiers.
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| Keywords
privacy-preserving
homomorphic encryption
classification algorithm
operations on encrypted data
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Issue Date: 27 April 2020
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2020,
Vol. 60
