CAREER: Sensible Privacy: Pragmatic Privacy Controls in an Era of Sensor-Enabled Computing

职业：敏感隐私：传感器计算时代的实用隐私控制

• 批准号: 1252697
• 负责人: Apu Kapadia
• 金额: $ 55.09万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252697&HistoricalAwards=false
• 关键词: CAREER; Sensible; Privacy; Pragmatic; Controls

Social networking and sensor-rich devices such as smartphones are becoming increasingly pervasive in today's society. People can share information concerning their location, activity, fitness, and health with their friends and family while benefiting from applications that leverage such information. Yet, users already find managing their privacy to be challenging, and the complexity involved in doing so is bound to increase. Usable techniques are required to enable people to effectively manage the dissemination of their private information as sensed by their mobile devices and sensors in their environment. This project explores `sensible' privacy controls and feedback mechanisms that allow people (or automated mechanisms acting on their behalf) to respond to unanticipated patterns and actual uses of their information in a way that is usable and intuitive. This approach has two advantages: 1) people need only care about the subset of data and usage scenarios that have the potential to violate their privacy, thus reducing the amount of data to which they must regulate access; and 2) people make better decisions concerning such access when these decisions are made in a context where they know how their data is being used. Sensible privacy mechanisms can have a profound and positive societal impact by not only helping people control their privacy, but also potentially increasing their participation in sensor-enabled computing because of this added control. This project firmly integrates education into the research through research experiences for underrepresented groups and the development of course modules on privacy at the undergraduate and graduate levels.

社交网络和智能手机等富含传感器的设备在当今社会变得越来越普遍。人们可以与朋友和家人分享有关他们的位置、活动、健身和健康的信息，同时从利用这些信息的应用程序中受益。然而，用户已经发现管理自己的隐私具有挑战性，而且这样做的复杂性必然会增加。需要可用的技术来使人们能够有效地管理其环境中的移动设备和传感器感知到的私人信息的传播。该项目探索“明智的”隐私控制和反馈机制，允许人们（或代表他们行事的自动化机制）以可用且直观的方式响应其信息的意外模式和实际使用。 这种方法有两个优点：1）人们只需要关心可能侵犯其隐私的数据子集和使用场景，从而减少他们必须规范访问的数据量； 2) 当人们在知道自己的数据如何被使用的情况下做出这些决策时，他们就可以就此类访问做出更好的决策。 合理的隐私机制可以产生深远而积极的社会影响，不仅可以帮助人们控制自己的隐私，而且由于这种额外的控制，还可能增加他们对传感器支持计算的参与。该项目通过针对代表性不足群体的研究经验以及开发本科生和研究生级别的隐私课程模块，将教育牢固地融入研究中。