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

CSR：大型：协作研究：在普适计算系统中实现隐私与效用的权衡

• 批准号: 1213140
• 负责人: Mani Srivastava
• 金额: $ 16.3万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1213140&HistoricalAwards=false
• 关键词: CSR; Large; Collaborative; Research; Enabling

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.

普及计算，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，无论是由用户发起，还是由服务提供商、政府实体、利益团体和个人等其他当局发起。嵌入在这些数据中的信息，即使使用复杂的数据挖掘算法，其他人也可以融合这些信息，构建我们活动的虚拟传记，揭示私人行为和生活方式。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官数据进行隐私控制。智力优势：该项目正在开发一种用户可配置的加密安全？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。为了使隐私更易于理解，我们向用户提供了一种更高层次的抽象，用于根据传感器测量得出的丰富推论和上下文来表达隐私和共享。用户可以将一些推论和上下文指定为私有。为了在保护隐私的同时确保感知信息接收者提供的服务质量，该系统还结合了算法，随着时间的推移，该算法可以从共享推断中学习隐私风险的个性化模型。理论概念和系统实现正在通过移动健康和个人传感的用户研究进行验证。更广泛的影响：通过更好地理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更多信息，并为共享感官信息的人提供有效的隐私管理工具。