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.

计算机视觉的进步使生物特征认证和识别变得比以前更加可靠和容易使用，这些数据的普遍使用潜力远远超出传统认证。保护高度敏感的生物识别数据的必要性是显而易见的，与用于身份验证目的的其他类型的数据不同，生物识别数据不能被撤销或替换为新值。这意味着生物识别数据安全性的任何破坏都会比其他敏感数据的丢失对个人造成更深远的影响。以前，只有有限的机制可以保护生物识别隐私。为了解决缺乏足够的保护机制的问题，该项目开发了新技术，用于在广泛的环境和应用程序中处理和使用生物识别数据时增强生物识别数据的隐私保护，这些环境和应用程序远远超出了比较范围。两个生物识别码。该项目的范围涵盖（i）生物识别数据的安全交互计算，（ii）将生物识别计算安全外包给不受信任的服务器，以及（iii）有效验证外包计算结果的正确性。这项工作的一个显着好处是，它将密码设计与对真实生物特征数据的实际评估相结合，以及将开发的工具集成到生物特征计算网格环境中。这项研究的结果预计将实现并促进处理敏感生物识别数据的更安全实践，包括利用不可信的计算能力，以及不允许共享数据的实体之间更广泛的协作。