Scholarlink

<p>Ascon, a family of algorithms supporting both authenticated encryption and hashing, has been selected by NIST as the standard for lightweight cryptography. In the analysis of Ascon-XOF and Ascon-Hash, Dobraunig et al. proposed a method to linearize the polynomials of the output by guessing some input variables to solve for the preimage of the hash value. Baek et al. improved this method by introducing a greedy search algorithm to reduce the guessed variables. However, none of them have provided the relationship between the number of linearized output bits and the minimum number of guessed variables. To address this issue, this paper utilizes the MILP tool to find the relationship between the number of linearized output bits and the minimum number of guessed variables for 2, 3, and 4 rounds Ascon-XOF, respectively. Based on this relationship, this paper presents improved preimage attacks on round-reduced Ascon-XOF using linearization technique. Compared to the results of Baek et al., the time complexity is reduced by 1/4, 1/8, and 1/2 for 2, 3, and 4 rounds Ascon-XOF, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improved preimage attacks on Ascon-XOF based on linearization technique

  • 期刊论文

  • 发表日期：

    2026年1月5日

查看全文
  • Shun Zhang,
  • Tairong Shi,
  • Senpeng Wang,
  • Jie Guan,
  • Mingyao Gao

摘要

Ascon, a family of algorithms supporting both authenticated encryption and hashing, has been selected by NIST as the standard for lightweight cryptography. In the analysis of Ascon-XOF and Ascon-Hash, Dobraunig et al. proposed a method to linearize the polynomials of the output by guessing some input variables to solve for the preimage of the hash value. Baek et al. improved this method by introducing a greedy search algorithm to reduce the guessed variables. However, none of them have provided the relationship between the number of linearized output bits and the minimum number of guessed variables. To address this issue, this paper utilizes the MILP tool to find the relationship between the number of linearized output bits and the minimum number of guessed variables for 2, 3, and 4 rounds Ascon-XOF, respectively. Based on this relationship, this paper presents improved preimage attacks on round-reduced Ascon-XOF using linearization technique. Compared to the results of Baek et al., the time complexity is reduced by 1/4, 1/8, and 1/2 for 2, 3, and 4 rounds Ascon-XOF, respectively.