DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is a powerful cryptographic primitive to secure outsourced computations against an untrusted third-party provider. With the growing demand for AI and the usefulness of machine learning as a service (MLaaS), the need for secure training and inference of artificial n...

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Veröffentlicht in:	IEEE transactions on information forensics and security 2021, Vol.16, p.3740-3752
Hauptverfasser:	Meftah, Souhail, Tan, Benjamin Hong Meng, Mun, Chan Fook, Aung, Khin Mi Mi, Veeravalli, Bharadwaj, Chandrasekhar, Vijay
Format:	Artikel
Sprache:	eng
Schlagworte:	Adders Algorithms Artificial intelligence Artificial neural networks Biological neural networks Circuit design Cloud computing Complexity Computational modeling Cryptography depth optimization Encryption Fully homomorphic encryption Machine learning Neural networks Neurons ReLu Table lookup
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