TC: Small: Trustworthy Mobile Sensing

消费者移动的设备，如智能手机，已被提出用于广泛的设置，包括公民新闻，公民科学，和国内老年人护理。 在这些领域中重新利用消费类设备是很有吸引力的，因为消费类设备为移动的传感提供了一个低成本、高度可扩展的平台，可以扩大覆盖范围并降低成本。与此同时，这些应用场景提出了新的数据完整性和隐私挑战。 组织目前依赖于非技术性的经济和法律的框架来建立对感知数据的信任：完整性准则规定了如何收集和处理数据，数据源通过具有约束力的法律的协议承担责任。不幸的是，这些框架不是匿名的，也不能扩展到全球数十亿的移动电话用户。可信的移动的传感提供了一种技术方法，可以在不牺牲匿名性的情况下确保数据完整性。本计画探讨使用可信协议硬体建立可信移动的感测平台。三个可信软件子系统位于TPM功能之上：1）传感器数据从传感硬件到不可信应用程序地址空间的可信路径，2）用于在不可信应用程序执行时透明监控不可信应用程序的可信运行时，以及3）用于将不可信应用程序的输入传感器数据与它们生成的数字工件进行比较的可信事后分析引擎。 总之，这种硬件和软件的组合为推理匿名用户生成内容的可信度提供了坚实的基础。

Consumer mobile devices such as smartphones have been proposed for use in a wide range of settings, including citizen journalism, citizen science, and domestic eldercare. Re-purposing consumer devices in these domains is appealing because consumer devices offer a low-cost, highly-scalable platform for mobile sensing that can expand coverage and reduce costs.At the same time, these application scenarios raise new data-integrity and privacy challenges. Organizations currently rely on non-technical economic and legal frameworks to establish trust in sensed data: integrity guidelines specify how data can be gathered and handled, and data sources are held accountable through binding legal agreements. Unfortunately, these frameworks are not anonymous and do not scale to the billions of world-wide mobile-phone users.Trustworthy mobile sensing offers a technical way to ensure data integrity without sacrificing anonymity. This project investigates building a platform for trustworthy mobile sensing using Trusted Platform Module (TPM) hardware. Three trusted software subsystems sit on top of TPM functionality: 1) a trusted path for sensor data from sensing hardware to untrusted applications' address spaces, 2) a trusted runtime for transparently monitoring untrusted applications as they execute, and 3) a trusted post-facto analysis engine for comparing untrusted applications' input sensor data to the digital artifacts they generate. Taken together, this combination of hardware and software provides a strong foundation for reasoning about the trustworthiness of anonymous, user-generated content.