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arXiv · 2408.07621

Information-Set Decoding for Convolutional Codes

Niklas Gassner·Julia Lieb·Abhinaba Mazumder·Michael Schaller
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Abstract

In this paper, we present a framework for generic decoding of convolutional codes, which allows us to do cryptanalysis of code-based systems that use convolutional codes. We then apply this framework to information set decoding, study success probabilities and give tools to choose variables. Finally, we use this to attack two cryptosystems based on convolutional codes. In the first, our code recovered about 74% of errors in less than 10 hours each, and in the second case, we give experimental evidence that 80% of the errors can be recovered in times corresponding to about 70 bits of operational security, with some instances being significantly lower.

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Niklas Gassner, Julia Lieb, Abhinaba Mazumder, Michael Schaller. 2024-08-14. Information-Set Decoding for Convolutional Codes. https://doi.org/10.1007/s10623-025-01649-1

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