Nested linear coding is a widely used technique in wireless communication systems for improving both security and reliability. Some parameters, such as the relative generalized Hamming weight and the relative dimension/length profile, can be used to characterize the performance of nested linear codes. In addition, the rank properties of generator and parity-check matrices can also precisely characterize their security performance. Despite this, finding optimal nested linear secrecy codes remains a challenge in the finite-blocklength regime, often requiring brute-force search methods. This paper investigates the properties of nested linear codes, introduces a new representation of the relative generalized Hamming weight, and proposes a novel method for finding the best nested linear secrecy code for the binary erasure wiretap channel by working from the worst nested linear secrecy code in the dual space. We demonstrate that our algorithm significantly outperforms the brute-force technique in terms of speed and efficiency.
This content will become publicly available on February 22, 2025
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
- Award ID(s):
- 2211428
- NSF-PAR ID:
- 10495855
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 32nd IEEE/ACM International Conference on Program Comprehension, RENE
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This release covers the state of the data and associated analysis code for determining code sharing between cryptocurrency codebases funded through the end of the original NSF CRII award. This material is based on work supported by the National Science Foundation under Grant CNS-1849729.
