Privacy Preserving Inference for Deep Neural Networks: Optimizing Homomorphic Encryption for Efficient and Secure Classification

Privacy Preserving Inference for Deep Neural Networks: Optimizing Homomorphic Encryption for Efficient and Secure Classification

The application of machine learning in healthcare, financial, social media, and other sensitive sectors not only involves high accuracy but privacy as well. Due to the emergence of the Cloud as a computation and one-to-many access paradigm; training and classification/inference tasks have been outso...

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Bibliographic Details
Main Authors: Aftab Akram, Fawad Khan, Shahzaib Tahir, Asif Iqbal, Syed Aziz Shah, Abdullah Baz
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
homomorphic encryption
activation function
cloud server
approximation techniques
security and privacy
Online Access:https://ieeexplore.ieee.org/document/10411911/

Internet

https://ieeexplore.ieee.org/document/10411911/

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