SaTC: CORE: Small: Enabling Mobile Users to Solve Privacy Issues with Their Own Hands Unobtrusively based on Gripping Hand Sensing

SaTC：核心：小型：使移动用户能够基于抓握手部感应，用自己的双手不引人注意地解决隐私问题

• 批准号: 2155131
• 负责人: Chen Wang
• 金额: $ 47.14万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2155131&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Enabling; Mobile

Smartphones have become our most intimate devices. To protect against unauthorized access, many authentication methods (e.g., PINs, fingerprints, and face IDs) have been deployed. However, by requiring active user efforts to input biometric data, they are unable to cope with many scenarios when user participation is not available at once, such as when the smartphone is shared with others or when notification content is displayed on the screen spontaneously. Furthermore, while biometric data have become more popular for authentication, it can be eavesdropped and reused. This research addresses the two long-standing issues of biometric authentication, obtrusiveness and replay threat, based on gripping-hand sensing. The key insight is that for a handheld device, the user needs to hold the device to use it. The project’s novelties are enabling smartphone users to solve access issues with their own hands unobtrusively, extracting nonreusable biometric features for authentication, and using media sounds to sense the user’s gripping hand. The project's broader significance and importance are that it benefits the huge population of smartphone users by providing enhanced mobile security, disseminates the research outcomes to the public to benefit both industry and academia, and contributes to educational programs, including curriculum development, graduate/undergraduate training, K-12 involvement, and underrepresented student engagement in research. This project advances the knowledge of mobile device security and privacy. It explores a novel gripping-hand biometric for user identification, which is readily available to acquire visually or acoustically from mobile device users. Deep learning-based algorithms are developed to derive unique biometric features and distinguish people’s hands. The project further devises a technique based on modulated stimulus signals to encode biometric features into each non-repeated biometric code for challenge-response user authentication. Moreover, cross-domain user authentication methods are developed, which leverage commonly used smartphone audio signals (e.g., ringtones) to sense and recognize a user’s gripping hand rather than using dedicated signals. The hard-to-forge physical relationships between the resulting acoustic and vibration responses of such audio signals are explored for achieving replay resistance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

智能手机已成为我们最亲密的设备。为了防止未经授权的访问，部署了许多身份验证方法（例如，引脚，指纹和面部ID）。但是，通过需要积极的用户努力输入生物识别数据，当用户参与一次不可用时，例如，当与他人共享智能手机或屏幕上显示智能手机时，他们就无法应对许多方案。此外，尽管生物特征数据在身份验证方面变得越来越流行，但它可以被窃听和重复使用。这项研究涉及基于手持感应的生物识别认证，伪装和重播威胁的两个长期问题。关键的见解是，对于手持设备，用户需要握住设备才能使用它。该项目的新颖性是使智能手机用户能够无需自己的手来解决访问问题，从而提取了不可再生的生物特征识别功能以进行身份​​验证，并使用媒体声音来感知用户的握手。该项目的更大意义和重要性是，它通过提供增强的移动安全性，将研究成果传播给公众，从而使行业和学术界受益，并为教育计划（包括课程发展，研究生/培训培训，K-12参与，K-12参与以及在研究中不足的学生参与研究），从而使大量智能手机使用者受益。该项目提高了移动设备安全性和隐私知识。它探索了一种用于用户识别的新颖的手工生物识别，可轻松从移动设备用户获得视觉或准确获取。基于深度学习的算法的开发是为了获得独特的生物特征特征并区分人们的手。该项目进一步设计了一种基于调制的刺激信号的技术，以将生物特征特征编码为每个未重复的生物识别代码，以进行挑战 - 响应用户身份验证。此外，开发了跨域用户身份验证方法，该方法利用常用的智能手机音频信号（例如铃声）来感知和识别用户的握住手，而不是使用专用信号。探索了此类音频信号所产生的声学和振动响应之间的难以实现的物理关系，以实现重播阻力。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。