Secret Key Distillation with Speech Input and Deep Neural Network-Controlled Privacy Amplification

Jelica Radomirović (), Milan Milosavljević, Zoran Banjac and Miloš Jovanović
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Jelica Radomirović: Vlatacom Institute of High Technology, Milutina Milankovica 5, 11070 Belgrade, Serbia
Milan Milosavljević: Vlatacom Institute of High Technology, Milutina Milankovica 5, 11070 Belgrade, Serbia
Zoran Banjac: Vlatacom Institute of High Technology, Milutina Milankovica 5, 11070 Belgrade, Serbia
Miloš Jovanović: Faculty of Information Technologies, Belgrade Metropolitan University, Tadeuša Košćuška 63, 11000 Belgrade, Serbia

Mathematics, 2023, vol. 11, issue 6, 1-22

Abstract: We propose a new high-speed secret key distillation system via public discussion based on the common randomness contained in the speech signal of the protocol participants. The proposed system consists of subsystems for quantization, advantage distillation, information reconciliation, an estimator for predicting conditional Renyi entropy, and universal hashing. The parameters of the system are optimized in order to achieve the maximum key distillation rate. By introducing a deep neural block for the prediction of conditional Renyi entropy, the lengths of the distilled secret keys are adaptively determined. The optimized system gives a key rate of over 11% and negligible information leakage to the eavesdropper, while NIST tests show the high cryptographic quality of produced secret keys. For a sampling rate of 16 kHz and quantization of input speech signals with 16 bits per sample, the system provides secret keys at a rate of 28 kb/s. This speed opens the possibility of wider application of this technology in the field of contemporary information security.

Keywords: speech signals; secret cryptographic key establishment; stylometric features; Huffman source coding; collision entropy; deep neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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