STARSS: Small: Trapdoor Computational Fuzzy Extractors

STARSS：小型：活板门计算模糊提取器

• 批准号: 1523572
• 负责人: Srini Devadas
• 金额: $ 26.67万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523572&HistoricalAwards=false
• 关键词: STARSS; Small; Trapdoor; Computational; Fuzzy

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.

模糊提取器将生物特征数据转换为可重复的均匀随机字符串，并可以将加密技术应用于生物识别安全性。它们用于使用来自生物识别输入的键进行加密和身份验证用户数据。 这项研究调查了硬件安全性原始图如何具有可证明的加密属性，该连接在很大程度上缺乏当前可用的硬件原始图。这样的计算模糊提取器的开发可能会导致更有效，更可靠的关键提取器，这些提取器可能会因其提高的效率和良好的安全性而被行业和其他利益相关者更好地接收。计算模糊提取器从包括硅生物识别源在内的生物特征来源及其安全性的键是基于问题（例如与噪声学习奇偶校验（LPN））的难度。现有的计算模糊提取器需要指数时间来提取键时，当生物特征源产生的位包含恒定的错误部分。该项目探讨了避免噪声的陷阱门的概念，该陷阱门会导致计算模糊提取器，该提取器可以在生物特征源产生的位置的恒定分数中纠正多项式时间的错误。安全性假设正是LPN计算硬度的假设。在对生物识别数据的假设较弱的假设下，这种方法与以前假定生物识别数据均匀分布的方案相比保持安全。该项目通过MIT Primes高中外展计划介绍了高中生，以研究应用密码和安全性。