Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation
Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation
复制标题
对设备特定的多位符号进行最大似然解码，以实现可靠的密钥生成
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Devadas
中科院分区：
文献类型：
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作者：
M. Yu;Matthias Hiller;S. Devadas
我们提出了一种PUF密钥生成方案，该方案对来自PUF响应比特的符号使用可证明最优的最大似然(ML)检测方法。基于制造差异，每个设备形成噪声的、特定于设备的符号星座。每个检测到的符号是纠错码的码字中的一个字母，从而产生非二进制码字。我们提出了一个三管齐下的验证策略：i.数学(推导出最优的符号解码器)，ii.模拟(与以前的方法进行比较)，以及经验性(使用执行数据)。我们给出的仿真结果表明，对于给定的PUF噪声水平和块大小(辅助数据大小的估计)，我们的新的基于符号的最大似然方法的误码率比先前的方案(如块编码、重复编码和基于阈值的模式匹配)高一个数量级，特别是在由于极端环境变化而导致的高噪声水平下。我们展示了一种基于ML符号的软判决纠错方法在28 nm硅片上的环境可靠性，覆盖-65°C到105°C的环境(包括125°C结)，128位密钥再生错误概率≤1 ppm。
We present a PUF key generation scheme that uses the provably optimal method of maximum-likelihood (ML) detection on symbols derived from PUF response bits. Each device forms a noisy, device-specific symbol constellation, based on manufacturing variation. Each detected symbol is a letter in a codeword of an error correction code, resulting in non-binary codewords. We present a three-pronged validation strategy: i. mathematical (deriving an optimal symbol decoder), ii. simulation (comparing against prior approaches), and iii. empirical (using implementation data). We present simulation results demonstrating that for a given PUF noise level and block size (an estimate of helper data size), our new symbol-based ML approach can have orders of magnitude better bit error rates compared to prior schemes such as block coding, repetition coding, and threshold-based pattern matching, especially under high levels of noise due to extreme environmental variation. We demonstrate environmental reliability of a ML symbol-based soft-decision error correction approach in 28nm FPGA silicon, covering -65°C to 105°C ambient (and including 125°C junction), and with 128bit key regeneration error probability ≤ 1 ppm.