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

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，已经部署了许多身份验证方法（例如pin，指纹和面部id）。然而，由于需要用户主动输入生物特征数据，他们无法应对许多无法同时获得用户参与的场景，例如当智能手机与他人共享或通知内容自发显示在屏幕上时。此外，虽然生物识别数据在身份验证中越来越流行，但它可能被窃听和重用。本研究解决了两个长期存在的问题，生物识别认证，突出性和重播威胁，基于手握传感。关键在于，对于手持设备，用户需要握住设备才能使用它。该项目的新奇之处在于，智能手机用户可以用自己的双手不显眼地解决访问问题，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紧的手。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安全，使大量智能手机用户受益，向公众传播研究成果，使工业界和学术界受益，并为教育项目做出贡献，包括课程开发，研究生/本科生培训，K-12参与，以及代表性不足的学生参与研究。该项目提高了对移动设备安全和隐私的认识。它探索了一种新的用于用户识别的握力手生物识别技术，可以很容易地从移动设备用户那里获得视觉或听觉信息。开发了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，开发了跨域用户身份验证方法，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持手，而不是使用专用信号。这种音频信号的声学和振动响应之间难以形成的物理关系被探索，以实现重放阻力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.