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再现的均匀随机字符串，并使其有可能应用于生物特征安全的密码技术。它们用于使用从生物特征输入中获得的密钥对用户数据进行加密和认证。 本研究探讨如何硬件安全原语可以有可证明的密码属性，这是目前可用的硬件原语在很大程度上缺乏连接。这种计算模糊提取器的开发可以导致实质上更有效和可靠的密钥提取器，由于其改进的效率和良好建立的安全属性，其可以更好地被工业和其他利益相关者接受。计算模糊提取器从生物特征源（包括硅生物特征源）获得密钥，其安全性基于诸如噪声学习奇偶校验（LPN）等问题的难度。现有的计算模糊提取器需要指数时间来提取密钥时，由生物特征源产生的位包含一个恒定的错误分数。该项目探讨了一个避免噪音的陷门的概念，导致计算模糊提取器，可以纠正错误的多项式时间在一个恒定的比例的位产生的生物特征源。安全性假设实际上就是对LPN计算难度的假设。这种方法仍然是安全的，在较弱的假设下，生物特征数据比以前的计划，假设均匀分布的生物特征数据。该项目通过MIT PRIMES高中外展计划向高中生介绍应用密码学和安全性研究。