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How to generate repeatable keys using physical unclonable functions: Correcting PUF errors with iteratively broadening and prioritized search.

机译:如何使用物理不可克隆功能生成可重复键:通过迭代扩展和优先搜索来纠正PUF错误。

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摘要

I present an algorithm for repeatably generating keys using entropy from a physical unclonable function (PUF). PUFs are logically identical devices with challenge- response pairs unique to each device. PUF errors inhibit key repeatability. My algorithm corrects PUF errors, enabling repeatable cryptographic key generation.;Repeatedly sampling the PUF and correcting errors with prioritized iteratively broadening and exhaustive search recreate seeds. Repeated sampling determines the most likely bit values and error probabilities. The search ends if a test indicates the seed is correct. The iteratively broadening search tests seeds with up to two errors. The exhaustive search tests seeds until the correct seed is found or failure is declared. PUF bit error rates prioritizes the searches. Previous algorithms often omit noisy PUF bits or use error correcting code and helper data. The presented algorithm uses all PUF bits regardless of noise. Non-volatile data for key regeneration is either a plaintext-ciphertext sample or, for public-key cryptography, the public key.;I implemented a latch-based PUF on FPGAs and measured PUF characteristics to analyze the effectiveness of the algorithm. Tests show repeated sampling nearly eliminating the probability of errors. However, the FPGA-based PUFs did not exhibit ideal behavior. Extrapolation to error rates reported by other publications shows relatively few samples drives the error probability to near zero. The probability is not zero. The iterative broadening and exhaustive searches further reduce failure rates.
机译:我提出了一种算法,该算法使用物理不可克隆函数(PUF)的熵来重复生成密钥。 PUF在逻辑上是相同的设备,每个设备具有唯一的质询-响应对。 PUF错误会抑制键的重复性。我的算法纠正了PUF错误,从而实现了可重复的加密密钥生成。重复采样PUF并通过优先迭代扩展和穷举搜索来纠正错误,从而重新创建了种子。重复采样确定最可能的比特值和错误概率。如果测试表明种子正确,则搜索结束。迭代地扩大搜索范围以最多两个错误测试种子。详尽的搜索会测试种子,直到找到正确的种子或声明失败为止。 PUF误码率优先搜索。先前的算法通常会忽略嘈杂的PUF位或使用纠错码和辅助数据。所提出的算法使用所有PUF位,而不管噪声如何。用于密钥再生的非易失性数据可以是纯文本-密文样本,对于公共密钥密码学而言,可以是公共密钥。我在FPGA上实现了基于锁存的PUF,并测量了PUF特性以分析算法的有效性。测试表明重复采样几乎消除了错误的可能性。但是,基于FPGA的PUF并没有表现出理想的行为。根据其他出版物报道的错误率推断,相对较少的样本将错误概率降至接近零。概率不为零。迭代拓宽和穷举搜索进一步降低了故障率。

著录项

  • 作者

    Price, Nathan.;

  • 作者单位

    University of Maryland, Baltimore County.;

  • 授予单位 University of Maryland, Baltimore County.;
  • 学科 Computer engineering.;Engineering.;Computer science.
  • 学位 M.S.
  • 年度 2014
  • 页码 51 p.
  • 总页数 51
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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