CSR: Small: Collaborative Research: Overheard at Home - Mitigating Overhearing of Continuous Listening Devices

与人类对话的智能家居设备日益普及。这些免提、联网的设备不断地倾听周围的声音，因此，无论何时有人对它们说话，它们都能立即做出回应。在家里有这种随时监听的设备时，人们的隐私受到侵犯的风险最大。例如，黑客可以侵入这些设备，监听用户在家里说的每句话或发出的每一个声音。这个项目的目标是阻止这样的攻击，并确保持续侦听设备不会无意中听到任何他们不应该听到的东西。本项目采用一种广义的方法来解决任何声传感系统的偷听问题。将开发一种可编程的声学滤波器，在音频信号进入不可信的声学传感系统之前，它将检查和过滤掉用户定义的声音。为了实现这一目标，该项目分为四个研究任务：(a)设计算法，在资源有限的嵌入式平台上分离大量声源；(b)设计算法实时计算活跃源的数量；(c)开发只需要很少训练数据的声音识别算法；(d)开发定制的、低功耗的微型嵌入式系统。保护隐私的移动医疗应用程序对于将个性化和低成本的医疗保健带给他们可以信任的大众非常重要。该系统将部署在两个基于音频的移动健康应用程序中：哮喘和睡眠监测，其中隐私是最重要的。在这个项目中开发的硬件和软件材料将在北卡罗来纳大学和哥伦比亚大学的研究生和本科生课程中使用。这个奖将资助一名女研究生。研究成果将在外展活动、在线课程和会议出版物中展示。匿名数据集、算法、代码和嵌入式系统的设计将通过网站（https://overhearing.web.unc.edu/）与研究社区共享。该网站位于北卡罗来纳大学教堂山分校。该存储库将对公众开放，并在整个3年的项目中保持最新状态。该网站将在项目结束后尽可能长时间保持活跃。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。

Smart home devices that converse with humans are becoming popular day by day. These hands-free, Internet-connected devices continuously listen to their surroundings, so that whenever someone speaks to them, they can respond right away. In the presence of such always-listening devices at home, people are at the greatest risk of privacy invasion. For instance, a hacker can hack into these devices and listen to everything one says or every sound one makes at home. The goal of this project is to thwart such attacks and to ensure that continuously listening devices do not overhear anything that they should not. This project takes a generalized approach to solve the overhearing problem of any acoustic sensing system. A programmable acoustic filter will be developed, which will inspect and filter out user-defined sounds before audio signals enter into an untrusted acoustic sensing system. To achieve this, the project is organized in four research tasks: (a) devising algorithms to separate a large number of sound sources on resource-constrained embedded platforms; (b) devising algorithms to count the number of active sources in real-time; (c) developing sound recognition algorithms that require very little training data; and (d) developing a custom, low-power, miniature embedded system. Privacy-preserving mobile health applications are important to bringing personalized and low-cost healthcare to the mass of people which they can trust. The system will be deployed in two audio-based mobile health applications: asthma and sleep monitoring, where privacy is of utmost importance. Hardware and software materials developed in this project will be used in graduate and undergraduate level courses at both University of North Carolina and Columbia University. This award will support a female graduate student. Research results will be demonstrated at outreach events, as well as through online course offerings, and conference publications. Anonymized datasets, algorithms, code, and the design of the embedded system will be shared with the research community through a website (https://overhearing.web.unc.edu/). The website is hosted at the University of North Carolina at Chapel Hill. The repository will be publicly accessible and will be kept up-to-date throughout the 3-year project. The website will remain alive beyond the project period for as long as possible.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.