TC: Small: Integrating Privacy Preserving Biometric Templates and Efficient Indexing Methods

生物识别技术，如指纹，为个性化身份验证提供了一个很好的工具。虽然人们通常愿意向政府机构提交自己的生物识别信息，但如果没有隐私保护的保证，他们就不太可能向商业公司提交自己的生物识别信息。该项目将通过引发大规模生物识别应用的广泛接受性，产生重大的社会影响。如今，大规模生物识别系统面临的主要技术障碍是解决两个相互竞争的目标的能力：(1)提供生物识别信息与存储生物识别读数的大型数据库的快速匹配；(2)提供数据库中生物识别信息的隐私，使其能够抵御恶意攻击。现有的解决方案解决了这两个问题中的一个，但不能同时解决这两个问题。以集成的方式解决这两个问题并非易事，因为隐藏生物特征数据以确保隐私的任何解决方案都涉及破坏生物特征模板中固有的结构，从而使有效地索引记录变得极具挑战性。该项目提出了一种统一的方法，通过开发“加密隐藏”的生物识别模板，使其能够快速搜索。这项工作将利用生物识别、数据库和编码理论等领域的新研究。

Biometrics, such as fingerprints, provide a great tool for personalized authentication. While people are usually willing to submit their biometric information to government agencies, they are less likely to do so for commercial companies without a guarantee of privacy protection. This project will have significant societal impact by triggering wide acceptability of large-scale biometrics enabled applications.The primary technological hurdle faced by large scale biometric systems today is the ability to address two competing objectives: (1) provide fast matching of a biometric against a large database of stored biometric readings and (2) provide privacy of the biometrics in the database such that it can withstand malicious attacks. Existing solutions tackle either of the two problem but not both simultaneously.Tackling the two problems in an integrated fashion is non-trivial as any solution to conceal the biometric data to ensure privacy involves breaking down the structure inherent in the biometric template, thus making it extremely challenging to index the records efficiently. This project presents a unified approach by developing "cryptographically hidden" biometric templates which will lend themselves to fast searches. This effort will draw upon new research in the fields of biometrics, databases, and coding theory.