A novel secure deep ensemble learning protocol based on Conjugacy search problem homomorphic encryption scheme


  • Luong The Dung
  • Hoang Duc Tho
  • Nguyen Hoang Anh
  • Tran Anh Tu




Deep learning, Privacy Preserving machine learning, secure multi-participant computation

Tóm tắt

Abstract— Nowadays, machine learning and deep learning have been widely employed. User privacy is an issue to consider in problems such as medicine, and finance. Machine learning models not only require accurate predictions but also ensure the privacy and security of data for users. In this paper, we propose a method to ensure the privacy for training and using deep learning models that employs a homomorphic encryption scheme based on the conjugate search problem. This method implements encryption on the data before transferring them to a cloud server, which stores local deep learning models from participants to predict the encrypted data, then the encrypted prediction results are sent back to users, and they perform decryption to get the model’s prediction result. These results can also be assembled to create a new training dataset for a model from the client. It is evident that our proposed model on the MNIST dataset produces an accuracy over 98% with some very simple network architectures and approximates the accuracy of centralized complex models, which does not ensure privacy.


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How to Cite

Dung, L. T., Tho, H. D., Anh, N. H., & Tu, T. A. (2022). A novel secure deep ensemble learning protocol based on Conjugacy search problem homomorphic encryption scheme. Journal of Science and Technology on Information Security, 1(15), 7-16. https://doi.org/10.54654/isj.v1i15.830