Abstract
The use of cryptographic functions has become vital for various devices, such as PCs, smart phones, drones, and smart appliances; however, the secure storage of cryptographic keys (or passwords) is a major issue. One way to securely store such a key is to register the key using secret data such as biometric data and then regenerate the key whenever it is needed. In this paper, we present a novel methodology for hiding cryptographic keys inside a deep neural network (DNN), and is termed as the DNN-based key hiding scheme. In this method, DNNs are constructed and trained with noisy data to hide the key within the network. To prove that our methodology works in practice, we propose an example of the DNN-based key hiding scheme and prove its correctness. For its robustness, we propose two basic security analysis tools to be able to check the example’s security. To the best of our knowledge, this is the first attempt of its kind.
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00033, Study on Quantum Security Evaluation of Cryptography based on Computational Quantum Complexity).
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References
Dodis, Y., Reyzin, L., Smith, A.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 523–540. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24676-3_31
Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. SIAM J. Comput. 38(1), 97–139 (2008)
Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: Proceedings of the 6th ACM Conference on Computer and Communications Security, pp. 28–36 (1999)
Jin, Z., Teoh, A., Goi, B., Tay, Y.: Biometric cryptosystems: a new biometric key binding and its implementation for fingerprint minutiae-based representation. Pattern Recogn. 56(13), 50–62 (2016)
Juels, A., Sudan, M.: A fuzzy vault scheme. Des. Codes Crypt. 38(2), 237–257 (2006)
Nakouri, I., Hamdi, M., Kim, T.H.: Chaotic construction of cryptographic keys based on biometric data. In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp. 509–516 (2016)
Itkis, G., Chandar, V., Fuller, B.W., Campbell, J.P., Cunningham, R.K.: Iris biometric security challenges and possible solutions: for your eyes only? Using the iris as a key. IEEE Signal Process. Mag. 32(5), 42–53 (2015)
Mariño, R.Á., Álvarez, F.H., Encinas, L.H.: A crypto-biometric scheme based on iris-templates with fuzzy extractors. Inf. Sci. 195, 91–102 (2012)
Sutcu, Y., Li, Q., Memon, N.: Design and analysis of fuzzy extractors for faces. In: Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, pp. 509–516 (2009)
Leng, L., Teoh, A.: Alignment-free row-co-occurrence cancelable palmprint fuzzy vault. Pattern Recogn. 48, 2290–2303 (2015)
Choi, D., Seo, S.H., Oh, Y.S., Kang, Y.: Two-factor fuzzy commitment for unmanned IoT devices security. IEEE Internet Things J. 6, 335–348 (2019)
Conti, V., Rundo, L., Militello, C., Mauri, G., Vitabile, S.: Resource-efficient hardware implementation of a neural-based node for automatic fingerprint classification. J. Wirel. Mob. Netw. Ubiquit. Comput. Dependable Appl. (JoWUA) 8, 19–36 (2017)
Li, Y., Ma, W.: Applications of artificial neural networks in financial economics: a survey. In: 2010 International Symposium on Computational Intelligence and Design, pp. 211–214 (2010)
De Mulder, W., Bethard, S., Moens, M.-F.: A survey on the application of recurrent neural networks to statistical language modeling. Comput. Speech Lang. 30, 61–98 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
Harrington, P.: Machine Learning in Action. Manning, Greenwich (2012)
Wallrabenstein, J.R.: Practical and secure IoT device authentication using physical unclonable functions. In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 99–106 (2016)
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016)
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Kim, T., Youn, TY., Choi, D. (2020). Is It Possible to Hide My Key into Deep Neural Network?. In: You, I. (eds) Information Security Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11897. Springer, Cham. https://doi.org/10.1007/978-3-030-39303-8_20
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DOI: https://doi.org/10.1007/978-3-030-39303-8_20
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