Secret-Key Agreement by Asynchronous EEG over Authenticated Public Channels

It is well known that Renyi’s entropy of order 2 determines the maximum possible length of the distilled secret keys in sequential secret key distillation protocols so that no information is leaked to the eavesdropper. There have been no attempts to estimate this key quantity based on information available to the legitimate parties to this protocol in the literature. We propose a new machine learning system, which estimates the lower bound of conditional Renyi entropy with high accuracy, based on 13 characteristics locally measured on the side of legitimate participants. The system is based on a prediction intervals deep neural network, trained for a given source of common randomness. We experimentally evaluated this result for two different sources, namely 14 and 6-dimensional EEG signals, of 50 participants, with varying advantage distillation and information reconciliation strategies with and without additional lossless compression block. Across all proposed systems and analyzed sources on average, the best machine learning strategy, called the hybrid strategy, increases the quantity of generated keys 2.77 times compared to the classical strategy. By introducing the Huffman lossless coder before the PA block, the loss of potential source randomness was reduced from 68.48% to a negligible 0.75%, while the leakage rate per one bit remains in the order of magnitude 10−4.

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Secret-Key Agreement by Asynchronous EEG over Authenticated Public Channels

Cited by 4 publications

References 29 publications

Modern techniques for decentralized key establishment in symmetric cryptographic systems

Modern techniques for decentralized key establishment in symmetric cryptographic systems

Cross Body Signal Pairing (CBSP): A Key Generation Protocol for Pairing Wearable Devices with Cardiac and Respiratory Sensors

Privacy Amplification Strategies in Sequential Secret Key Distillation Protocols Based on Machine Learning

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