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.

智能手机已经成为我们最亲密的设备。为了防止未经授权的访问，许多认证方法（例如，PIN、指纹和面部ID）已经部署。然而，由于需要用户主动输入生物特征数据，当用户参与不能立即获得时，例如当智能手机与其他人共享时或当通知内容自发地显示在屏幕上时，它们无法科普许多场景。此外，虽然生物特征数据在身份验证中变得越来越流行，但它可以被窃听和重复使用。这项研究解决了两个长期存在的问题，生物认证，侵扰和重放威胁，基于握手传感。该项目的创新之处在于，智能手机用户可以用自己的手轻松解决访问问题，提取不可重复使用的生物特征进行身份验证，并使用媒体声音来感知用户握着手机的手。该项目更广泛的意义和重要性在于，它通过提供增强的移动的安全性使大量智能手机用户受益，将研究成果传播给公众以使工业界和学术界受益，并为教育计划做出贡献，包括课程开发，研究生/本科生培训，K-12参与以及参与研究的学生人数不足。该项目推进了对移动终端安全和隐私的认识。它探讨了一种新的握手生物特征的用户识别，这是很容易获得的视觉或听觉从移动终端用户。开发基于深度学习的算法来获得独特的生物特征并区分人们的手。该项目进一步设计了一种基于调制刺激信号的技术，将生物特征编码到每个非重复的生物特征代码中，用于挑战-响应用户认证。此外，开发了跨域用户认证方法，其利用常用的智能电话音频信号（例如，铃声）来感测和识别用户抓握的手，而不是使用专用信号。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。