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Journal Article
Deep Neural Networks Based Key Concealment Scheme
- Authors
- Taehyuk Kim, Taek Young Youn, Dooho Choi
- Issue Date
- 2020-11
- Citation
- IEEE Access, v.8, pp.204214-204225
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- To keep the Internet-of-things (IoT) environment secure, employing a cryptographic function to various IoT devices has become vital. An important factor to consider is how to store a cryptographic key (or passwords) securely. A popular method is to store the key in the storage protected by some hardware-based security functions. This paper presents a novel concept to conceal cryptographic keys into deep neural networks (DNNs), named DNNs-based key concealment scheme. In this scheme, a key can be concealed into a proper deep neural network model which is trained with secret input data. We demonstrate the practical applicability of our concept by presenting an instance and a use-case scenario of the DNNs-based key concealment scheme and show its correctness. To prove its robustness, two fundamental security evaluation methods are proposed for investigating the security of the instantiation. To the best of our knowledge, this is the first attempt of its kind.
- KSP Keywords
- Deep neural network(DNN), Evaluation method, IoT devices, Neural network model, Security Evaluation, Use cases, cryptographic key, hardware-based security, input data, internet of things(IoT), neural network(NN)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
