Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II

Eric Ruzomberka, Homa Nikbakht, Christopher G. Brinton, David J. Love, H. V. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We revisit the binary adversarial wiretap channel (AWTC) of type II in which an active adversary can read a fraction r and flip a fraction p of codeword bits. The semantic-secrecy capacity of the AWTC II is partially known, where the best-known lower bound is non-constructive, proven via a random coding argument that uses a large number (that is exponential in blocklength n) of random bits to seed the random code. In this paper, we establish a new derandomization result in which we match the best-known lower bound of 1 - H2(p) - r, where H2(•) is the binary entropy function, via a random code that uses a small seed of only O(n2) bits. Our random code construction is a novel application of pseudolinear codes - a class of non-linear codes that have k-wise independent codewords when chosen at random where k is a design parameter. As the key technical tool in our analysis, we provide a soft-covering lemma in the flavor of Goldfeld, Cuff and Permuter (Trans. Inf. Theory 2016) that holds for random codes with k-wise independent codewords.

Original languageEnglish (US)
Title of host publication2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350328141
DOIs
StatePublished - 2023
Event59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023 - Monticello, United States
Duration: Sep 26 2023Sep 29 2023

Publication series

Name2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

Conference

Conference59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023
Country/TerritoryUnited States
CityMonticello
Period9/26/239/29/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Computational Mathematics
  • Control and Optimization

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