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

基本信息

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

项目摘要

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.

普遍的计算，例如智能手机，建筑物，汽车和城市中的传感器，都会增加传感器数据的共享，无论是用户还是由用户或其他当局（例如服务提供商，政府实体，利益集团和个人）的发起。嵌入在此数据中的信息是其他信息，即使使用复杂的数据挖掘算法，其他人也可以融合以构建我们活动的虚拟传记，从而揭示私人行为和生活方式模式。该项目中的研究人员正在设计计算方法，以使用户对共享其个人感觉数据进行隐私控制。智能优点：该项目正在开发一个用户可配置的密码安全性？隐私保护？在智能手机上运行并采用流向其他用户，应用程序和服务的传感器信息。为了使隐私可以理解，用户以更高级别的抽象来表达隐私和根据传感器测量的丰富推论和上下文共享。用户可以将一些推论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，随着时间的流逝，该算法通过共享推论来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方面的用户研究来验证。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数据的固有风险，该项目的结果将导致更受过教育和知情的公民，监管机构和政策制定者，并为共享感官信息的人提供有效的隐私管理工具。