Convolutional neural network designed as small truth tables, application to cryptography, formal verification & explainability

Convolutional neural network designed as small truth tables, application to cryptography, formal verification & explainability

DCNNs have revolutionized machine learning (ML) and deep learning (DL), achieving remarkable success in image recognition, natural language processing, and other domains. However, their deployment in security-critical industries such as cybersecurity, energy, finance, and the military faces persiste...

Full description

Bibliographic Details
Main Author: Benamira, Adrien
Other Authors: Thomas Peyrin
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Mathematical Sciences
Deep convolutional neural network
Machine/deep learning
Cryptanalysis
Neural distinguisher
Exact global xplainability
Side-channel attacks
Fully homomorphic encryption
Online Access:https://hdl.handle.net/10356/177203

Internet

https://hdl.handle.net/10356/177203

Similar Items