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可靠和更容易获得，这种数据的普遍使用可能远远超出传统认证。显然需要保护高度敏感的生物特征，与用于身份验证目的的其他类型的数据不同，生物特征不能被撤销或用新值替换。这意味着，任何违反生物特征数据安全的行为对个人的影响都远远超过其他敏感数据的丢失。以前，只有有限的机制可以保护生物特征数据的隐私。为了解决缺乏足够的保护机制，该项目开发了新的技术，用于增强生物特征数据的隐私保护，当它被处理和使用在环境中不完全信任或安全的广泛的上下文和应用程序，远远超出了比较两个生物特征代码。该项目的范围包括（i）生物特征数据的安全交互式计算，（ii）将生物特征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。这项工作的一个明显的好处是，它结合了加密设计与实际评估的真实的生物特征数据，以及包括集成开发的工具到一个生物特征计算网格环境。这项研究的成果预计将促进处理敏感生物特征数据的更安全做法，包括利用不受信任的计算能力，以及不允许共享数据的实体之间更广泛的合作。