Machine Learning over Encrypted Data With Fully Homomorphic Encyption

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2022-8-26

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Kahya, Ayşegül

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When machine learning algorithms train on a large data set, the result will be more realistic. Big data, distribution of big data, and the study of learning algorithms on distributed data are popular research topics of today. Encryption is a basic need, especially when storing data with a high degree of confidentiality, such as medical data. Classical encryption methods cannot meet this need because when texts encrypted with classical encryption methods are distributed, and the distributed data set is decrypted using the same key, the result is corrupted. Homomorphic encryption methods are suitable for this job because they allow polynomial operations on the ciphertext. The encryption key distribution to all these devices is a problem here, and the reliability of these devices' access to highly confidential plain text needs to be evaluated. Since homomorphic encryption allows polynomial operations and some machine learning algorithms are polynomial-based, training machine learning algorithms directly on the ciphertext could be a solution. Logistic regression is one of the polynomial-based machine learning algorithms. In this thesis, a logistic regression algorithm is trained on a data set containing various patient information. It was seen that the algorithm predicted with a 77.2 percent success rate whether people would be diagnosed with diabetes within five years or not. Afterward, the data set was encrypted using the CKKS fully homomorphic encryption method. While working logistic regression over the encrypted dataset, it is needed to use some approximations on the algorithm. We wanted to see the results of the applied approximation without any encryption first. And the learning algorithm was run again on the encrypted data set. And the successful prediction rate was 76.8. After the encryption, the algorithm predicted whether people would be diagnosed with diabetes in five years, and the correct prediction rate was 76.8 percent again. This showed us that machine learning with approximated logistic regression method could be run directly on a data set encrypted using the homomorphic encryption method.

Subject Keywords

homomorphic encryption, fully homomorphic encryption, CKKS, ma- chine learning, logistic regression

URI

https://hdl.handle.net/11511/98596

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Graduate School of Applied Mathematics, Thesis

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Citation Formats

A. Kahya, “Machine Learning over Encrypted Data With Fully Homomorphic Encyption,” M.S. - Master of Science, Middle East Technical University, 2022.