TWC: Small: Protecting Privacy of Biometric Data throughout Computation

TWC：小：在整个计算过程中保护生物识别数据的隐私

• 批准号: 1223699
• 负责人: Marina Blanton
• 金额: $ 35.02万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1223699&HistoricalAwards=false
• 关键词: TWC; Small; Protecting; Privacy; Biometric

Progress in computer vision makes biometric authentication and recognition become more reliable and readily available than before, with a potential for ubiquitous use of such data well beyond traditional authentication. The need to protect highly sensitive biometrics is apparent, which, unlike other types of data used for authentication purposes, cannot be revoked and replaced with a new value. This means that any breach in security of biometric data has far reaching consequences to the individual than loss of other sensitive data.Previously, only limited mechanisms for protecting privacy of biometry were available. To address the paucity of adequate protection mechanisms, this project develops novel techniques for enhancing privacy protection of biometric data when it is being processed and used in environments which are not fully trusted or secured in a broad set of contexts and applications that go well beyond comparing two biometric codes. The project's scope covers (i) secure interactive computation on biometric data, (ii) secure outsourcing of biometric computation to untrusted servers, and (iii) efficient verification of the correctness of the result of outsourced computation. A distinct benefit of this work is that it combines cryptographic design with practical evaluation on real biometric data, as well as includes integration of developed tools into a biometric computing grid environment. The outcomes of this research are expected to enable and promote safer practices in handing sensitive biometric data, including utilizing untrusted computing power, and a wider degree of collaboration between entities who are otherwise not permitted to share their data.

计算机视觉的进展使生物识别认证和识别变得比以前更可靠，更容易获得，并且潜在地使用了远远超出传统身份验证的此类数据。保护高度敏感的生物识别技术的需求显而易见，与用于身份验证目的的其他类型的数据不同，不能被撤销并替换为新值。这意味着，任何违反生物识别数据的违反，都对个人产生的后果远远超过了其他敏感数据的损失。为了解决足够的保护机制的匮乏，该项目开发了新的技术，以增强生物识别数据的隐私保护并在不完全信任或在广泛的上下文和应用程序中使用的环境中使用，这些环境远远超出了比较两个生物识别代码。该项目的范围涵盖了（i）对生物识别数据的安全交互式计算，（ii）生物识别计算的安全外包到不信任的服务器，以及（iii）有效验证外包计算结果的正确性。这项工作的一个明显好处是，它将加密设计与对实际生物识别数据的实际评估结合在一起，并包括将开发的工具集成到生物识别计算网格环境中。这项研究的结果有望在传递敏感的生物识别数据方面实现和促进更安全的做法，包括利用不信任的计算能力，以及否则不允许共享其数据的实体之间的更广泛的协作程度。