Current Result Document :
| 한글제목(Korean Title) |
A STUDY OF USING CKKS HOMOMORPHIC ENCRYPTION OVER THE LAYERS OF A CONVOLUTIONAL NEURAL NETWORK MODEL |
| 영문제목(English Title) |
A STUDY OF USING CKKS HOMOMORPHIC ENCRYPTION OVER THE LAYERS OF A CONVOLUTIONAL NEURAL NETWORK MODEL |
| 저자(Author) |
Sebastian Soler Castaneda
Kevin Nam
Youyeon Joo
Yunheung Paek
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| 원문수록처(Citation) |
VOL 29 NO. 01 PP. 0161 ~ 0164 (2022. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Homomorphic Encryption (HE) schemes have been recently growing as a reliable solution to preserve users’ information owe to maintaining and operating the user data in the encrypted state. In addition to that, several Neural Networks models merged with HE schemes have been developed as a prospective tool for privacypreserving machine learning. Those mentioned works demonstrated that it is possible to match the accuracy of non-encrypted models but there is always a trade-off in the computation time. In this work, we evaluate the implementation of CKKS HE scheme operations over the layers of a LeNet5 convolutional inference model, however, owing to the limitations of the evaluation environment, the scope of this work is not to develop a complete LeNet5 encrypted model. The evaluation was performed using the MNIST dataset with Microsoft SEAL(MSEAL) open-source homomorphic encryption library ported version on Python (PyFhel). The behavior of the encrypted model, the limitations faced and a small description of related and future work is also provided. |
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