SBIR Phase I: IMPULSE: Implicit private authentication with ultrasonic signals from mobile ecosystem

这一小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将来自于使用行为生物识别技术为移动的生态系统实现安全、可扩展、抗对抗、完全私密的身份验证，从而使消费者能够控制数字隐私。直接的影响将是减轻基于移动的欺诈以及努力构建符合GDPR的一对多生物识别技术的公司的财务负担。除了欺诈检测应用之外，开发用于从智能手机被动推断多个用户生物签名的技术将能够开发非侵入性和具有成本效益的远程医疗解决方案，从而使用可穿戴生理监测器，Wi-Fi和雷达传感器推进最先进的健康监测。该项目的影响将更广泛地产生于各种教育和推广活动，包括实习，指导和参加鼓励多样性的研讨会和会议，并通过与代表性不足的社区的教师和学生建立研究和教育合作。这个小企业创新研究第一阶段项目解决了长期存在的挑战，确保一对多生物识别系统，广泛应用于医院，边境管制设施等。最近的网络安全攻击和隐私侵犯使得开发一种对抗性技术变得至关重要，这种技术可以实现大规模的安全生物特征认证，同时为用户提供严格的隐私保证。因此，这项工作将在关键领域推进最先进的技术：（1）使用深度学习模型从高频声波中被动和非侵入性地提取出一类新的可充分区分的独特行为生物签名;（2）高效的隐私保护算法，以实现完全私密的生物特征识别，同时确保数据实用性和更快的相似性搜索;（3）对机器学习框架进行压力测试的新技术，以发现行为生物识别中的新攻击向量。这项技术将通过用户行为生物签名来增强生物识别技术，这些生物签名完全被动地来自智能手机上的音频传感器，使用深度迁移学习算法，以阻止欺骗和对抗性攻击;此外，它还将把敏感的生物特征标识符转换为可识别和匿名的标识符，本地差异隐私和基于散列的隐私该奖项反映了NSF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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from enabling a secure, scalable, adversarial-resistant, fully private authentication for mobile ecosystems using behavioral biometrics, thereby enabling consumer control of digital privacy. An immediate impact will be the mitigation of mobile based fraud as well as financial burden for companies struggling to build GDPR compliant one-to-many biometric technologies. Besides fraud detection applications, the technology developed to infer multiple user biosignatures passively from smartphones will enable the development of non-invasive and cost-effective telehealth solutions, thereby advancing the state-of-the-art monitoring of health using wearable physiological monitors, Wi-Fi, and radar sensors. Impacts of the project more broadly will result from various education and outreach activities, including internships, mentoring, and participation in seminars and conferences that encourage diversity, and by establishing research and education collaboration with faculty and students in underrepresented communities.This Small Business Innovation Research Phase I project addresses the long-standing challenge in securing one-to-many biometric systems, widely used in hospitals, border control infrastructure and more. Recent cyber security attacks and privacy violations makes it critical to develop an adversarial resistant technology that enables secure biometric authentication at scale, while at the same time providing rigorous privacy assurance to users. Consequently, this effort will advance the state-of-the-art in key fronts: (1) a new class of sufficiently distinguishable unique behavioral biosignatures extracted passively and non-invasively from high frequency sound waves using deep learning models; (2) efficient privacy-preserving algorithms to enable fully private biometrics while ensuring data utility and faster similarity search; (3) novel techniques to stress-test the machine learning framework in order to discover new attack vectors in behavioral biometrics. This technology will enhance biometrics with user behavioral biosignatures which are exclusively and passively derived from audio sensors on smartphones, using deep transfer learning algorithms, to thwart spoofing and adversarial attacks; furthermore, it will transform sensitive biometric identifiers into revocable and anonymized identifiers, with local differential privacy and hashing-based privacy-preserving algorithms.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.