CSR: Large: Collaborative Research: Enabling Privacy-Utility Trade-Offs in Pervasive Computing Systems

普适计算，例如智能手机、建筑物、汽车和城市中的传感器，导致传感器数据的共享增加，无论是由用户还是由诸如服务提供商、政府实体、利益集团和个人等其他机构发起。嵌入这些数据中的信息，即使使用复杂的数据挖掘算法，也可以融合到构建我们活动的虚拟传记中，揭示私人行为和生活方式模式。该项目的研究人员正在设计计算方法，让用户对共享的个人感觉数据进行隐私控制。智力优势：该项目正在开发一个用户可配置的密码安全？隐私保护在智能手机上运行，并根据流向其他用户、应用程序和服务的传感器信息采取行动。为了使隐私可以理解，用户呈现了更高级别的抽象，用于表达隐私和从传感器测量中得出的丰富推断和上下文方面的共享。用户可以将一些推理和上下文指定为私有。 为了提供隐私，同时确保感官信息的接收者提供的服务质量，该系统还结合了算法，该算法随着时间的推移，从共享推断中学习隐私风险的个性化模型。通过对移动的健康和个人感知的用户研究，验证了理论概念和系统实现。更广泛的影响：通过更好地了解行为隐私问题和共享看似无害的数据所固有的风险，该项目的结果将使公民，监管机构和政策制定者受到更多教育和了解，并为那些共享感官信息的人提供有效的隐私管理工具。

Pervasive computing, such as sensors in smartphones, buildings, automobiles and cities, result in increased sharing of sensor data, whether initiated by users or by other authorities such as service providers, government entities, interest groups, and individuals. Embedded in this data is information which others, even using sophisticated data mining algorithms, can fuse to construct a virtual biography of our activities, revealing private behaviors and lifestyle patterns. Researchers in this project are devising computational methods to let users exercise privacy control over their personal sensory data that is shared.Intellectual Merit: The project is developing a user-configurable cryptographically-secure ?privacy shield? to run on smartphones and act upon sensor information flowing to other users, apps, and services. To make privacy understandable, the user is presented with a higher level abstraction for expressing privacy and sharing in terms of rich inferences and contexts drawn from sensor measurements. The user can designate some inferences and contexts as private. To provide privacy while ensuring the quality of service provided by the recipients of the sensory information, the system also incorporates algorithms which, over time, learn a personalized model of the privacy risk from sharing an inference. The theoretical concepts and the system realization are being validated via user studies in mobile health and personal sensing. Broader Impacts: By providing better understanding of the behavioral privacy problem and risks inherent in sharing seemingly innocuous data, results from this project will lead to a more educated and informed citizenry, regulators, and policy makers, and provide effective tools for privacy management to those who share sensory information.