Privacy Amplification Strategies in Sequential Secret Key Distillation Protocols Based on Machine Learning
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 av...
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Published in | Symmetry (Basel) Vol. 14; no. 10; p. 2028 |
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Format | Journal Article |
Language | English |
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Abstract | 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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AbstractList | 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. |
Author | Milosavljević, Milan Kovačević, Branko Radomirović, Jelica Jovanović, Miloš |
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Cites_doi | 10.1109/ACCESS.2016.2521718 10.1002/j.1538-7305.1949.tb00928.x 10.1109/JRPROC.1952.273898 10.1145/73007.73009 10.1103/PhysRevA.67.052303 10.1016/0022-0000(79)90044-8 10.1109/ACCESS.2018.2871713 10.1109/GLOBECOM46510.2021.9685523 10.1109/COMST.2018.2812301 10.1137/0217014 10.1007/3-540-39799-X_37 10.1109/18.243431 10.1109/TIT.2003.809559 10.1109/GLOBECOM46510.2021.9685855 10.1109/TNN.2010.2096824 10.1002/sec.1192 10.1109/18.256484 10.1109/TIT.2004.838380 10.1109/18.476316 10.1109/TCOMM.2018.2814607 10.1145/3023954 10.1007/3-540-48969-X_10 10.1017/CBO9780511977985 10.37247/ETNI2ED.2.22.4 |
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Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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SubjectTerms | advantage distillation Artificial neural networks CASCADE Communication Design Distillation EEG Electroencephalography Entropy (Information theory) information reconciliation key distillation Leaking of information Lower bounds Machine learning Neural networks Privacy Randomness Reconciliation |
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Title | Privacy Amplification Strategies in Sequential Secret Key Distillation Protocols Based on Machine Learning |
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