Abstract
Key reconciliation protocols are critical components to deploy secure cryptographic primitives in practical applications. In this paper, we demonstrate on these new requirements and try to explore a new design routine in solving the key reconciliation problem in large scale p2p networks with automatic intelligent end user under the notion of evolvable cryptography. We design a new evolvable key reconciliation mechanism (KRM) based on two tricks for the AI user: the observation of shared beacons to evolve based on a deep auto-encoder, and the exchange of observed features as a hint to reconcile a shared key based on a deep paired decoder. For any passive adversary, the KRM is forward provable secure under the linear decoding hardness assumption. Compared with existing schemes, the performance evaluation showed our KRM is practical and quite efficient in communication and time costs, especially in multi-party scenarios.
Supported by the National Natural Science Foundation of China (No. 61572521, U1636114), National Key Project of Research and Development Plan (2017YFB0802000), Innovative Research Team Project of Engineering University of APF (KYTD201805), Fundamental Research Project of Engineering University of APF (WJY201910).
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Zhu, S., Han, Y., Yang, X., Wu, X. (2020). Efficient and Evolvable Key Reconciliation Mechanism in Multi-party Networks Based on Automatic Learning Structure. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_7
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