• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.826-845
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• 2017

Outsourcing computation is one of the most important applications in cloud computing, and it has a huge ability to satisfy the demand of data centers. Modular exponentiation computation, broadly used in the cryptographic protocols, has been recognized as one of the most time-consuming calculation operations in cryptosystems. Previously, modular exponentiations can be securely outsourced by using two untrusted cloud servers. In this paper, we present two practical and secure outsourcing modular exponentiations schemes that support only one untrusted cloud server. Explicitly, we make the base and the index blind by putting them into a matrix before send to the cloud server. Our schemes provide better performance in higher efficiency and flexible checkability which support single cloud server. Additionally, there exists another advantage of our schemes that the schemes are proved to be secure and effective without any cryptographic assumptions.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.161-164
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• 2022

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 privacy-preserving 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.861-868
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• 2023

One of the key technologies in providing data analysis in the deep learning while maintaining security is fully homomorphic encryption. Due to constraints in operations on fully homomorphically encrypted data, non-arithmetic functions used in deep learning must be approximated by polynomials. Until now, the degrees of approximation polynomials with composite minimax polynomials have been uniformly set across layers, which poses challenges for effective network designs on fully homomorphic encryption. This study theoretically proves that setting different degrees of approximation polynomials constructed by composite minimax polynomial in each layer does not pose any issues in the inference on convolutional neural networks.