Privacy-Preserving Classification Method for Neural-Biomarkers using Homomorphic Residue Number System CNN: HoRNS-CNN
2022 International Conference on Business Analytics for Technology and Security (ICBATS)(2022)
摘要
The increasing generation of MRI dataset and the recent cloud deployment of deep learning (DL) algorithms have enabled timely remote classifications of discrepancies in neural-biomarkers of critical health conditions such as dyslexia. Using these untrusted platforms to implement a secure DL algorithm will identify and resolve potential security attacks or patient data theft, hence, the need for a privacy-preserving method. However, existing homomorphic (FHE) privacy-preserving methods are still inefficient in terms of accuracy, latency, and feature extraction time with significantly large cipher-image expansion problem. This study proposes homomorphic residue number system-CNN (HoRNS-CNN) model for the privacy-preserving classification of dyslexia neural-biomarkers. The HoRNS-CNN architecture is composed of the RNS-FHE scheme and pre-trained CNN models. The RNS-FHE scheme was used to design an encryption module for each pixel in the dataset, while pre-trained CNNs were applied directly to the encrypted data in the cloud after adapting their activation layers to homomorphic computations. Results from proposed HoRNS-CNN model demonstrated an improved performance.
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关键词
Privacy-preserving methods,fully homomorphic encryption,residue number system,HoRNS-CNN,dyslexia detection
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