STARSS: Small: Trapdoor Computational Fuzzy Extractors

模糊抽取器将生物特征数据转换为可复制的均匀随机串，并使将密码技术应用于生物特征安全成为可能。它们用于使用从生物特征输入派生的密钥来加密和验证用户数据。这项研究调查了硬件安全基元如何具有可证明的密码学属性，这是目前可用的硬件基元所缺乏的一种联系。这种计算型模糊萃取器的开发可以大大提高密钥萃取器的效率和可靠性，因为它们的效率更高，安全性也更好，可能会更好地为工业界和其他利益攸关方所接受。计算型模糊抽取器从包括硅生物特征源在内的生物特征源中获取密钥，其安全性基于学习带噪声奇偶校验(LPN)等问题的难度。当生物特征源生成的比特包含恒定比例的错误时，现有的计算型模糊抽取器需要指数时间来提取密钥。该项目探索了避免噪声的陷门的概念，它导致了一种计算型模糊抽取器，它可以在多项式时间内纠正由生物特征源产生的比特的恒定分数中的错误。安全性假设就是LPN的计算难度假设。与以往假设生物特征数据均匀分布的方案相比，该方法在较弱的生物特征数据假设下仍然是安全的。该项目通过麻省理工学院的PRIMES高中推广计划，向高中生介绍应用密码学和安全方面的研究。

Fuzzy extractors convert biometric data into reproducible uniform random strings, and make it possible to apply cryptographic techniques for biometric security. They are used to encrypt and authenticate user data with keys derived from biometric inputs. This research investigates how hardware security primitives can have provable cryptographic properties, a connection which is largely lacking in currently available hardware primitives. The development of such computational fuzzy extractors could result in substantially more efficient and reliable key extractors which may be better received by industry and other stakeholders, due to their improved efficiency and well-established security properties. Computational fuzzy extractors derive keys from biometric sources including silicon biometric sources, and their security is based on the difficulty of problems such as Learning Parity With Noise (LPN). Existing computational fuzzy extractors require exponential time to extract keys when the bits generated by the biometric source contain a constant fraction of errors. The project explores the concept of a noise-avoiding trapdoor that results in a computational fuzzy extractor that can correct errors in polynomial time in a constant fraction of the bits generated by the biometric source. The security assumption is exactly the assumption of computational hardness of LPN. This approach remains secure under weaker assumptions about biometric data than previous schemes which assumed uniform distributions of biometric data. The project introduces high-school students to research in applied cryptography and security through the MIT PRIMES high-school outreach program.