Research Article
Noya: An Efficient, Flexible and Secure CNN Inference Model Based on Homomorphic Encryption
@INPROCEEDINGS{10.1007/978-3-031-94445-1_6, author={FengYuan Qiu and Hao Yang and Lu Zhou and Zhe Liu}, title={Noya: An Efficient, Flexible and Secure CNN Inference Model Based on Homomorphic Encryption}, proceedings={Security and Privacy in Communication Networks. 20th EAI International Conference, SecureComm 2024, Dubai, United Arab Emirates, October 28--30, 2024, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2026}, month={9}, keywords={Privacy-Preserving Neural Networks Homomorphic Encryption Convolutional Neural Networks Secure Inference}, doi={10.1007/978-3-031-94445-1_6} }- FengYuan Qiu
Hao Yang
Lu Zhou
Zhe Liu
Year: 2026
Noya: An Efficient, Flexible and Secure CNN Inference Model Based on Homomorphic Encryption
SECURECOMM
Springer
DOI: 10.1007/978-3-031-94445-1_6
Abstract
Machine Learning as a Service (MLaaS) become a popular application in cloud-based services, where users upload their data to a service provider and the provider runs a pre-learned model to give predictions back. However, MLaaS has a risk of privacy leakage cause the data transferred may contain users’ sensitive information. Homomorphic Encryption (HE) is a promising way to handle this problem. It allows the server to make calculations directly on encrypted data, and so guarantees the confidentiality of data. Unluckily, inference on encrypted data usually has massive time consumption and high memory requirement.
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