TWC: Small: PERMIT: Privacy-Enabled Resource Management for IoT Networks

TWC：小型：PERMIT：物联网网络的启用隐私的资源管理

• 批准号: 1617849
• 负责人: Anand Sarwate
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617849&HistoricalAwards=false
• 关键词: TWC; Small; PERMIT; Privacy; Enabled

This project investigates how privacy can be used to inform the design and management of future data sensing systems. Networked systems that collect data about individuals will play an increasingly important role in our lives, with applications including industrial monitoring and control, "smart" homes/cities, and personalized health care. These systems will gather private information about individuals, which creates many coupled engineering challenges. This work seeks to understand the interplay between two of these: managing the massive amounts of data that must be collected and protecting the privacy of individuals in the system. For applications or services that rely on populations of individuals, reducing the amount of information transmitted can save bandwith while enhancing privacy. The objectives of this work are to use ideas from data privacy technologies and wireless resource management techniques to jointly manage privacy and bandwidth in wireless sensing systems.The technical objectives of this project involve reformulating distributed data collection and estimation problems under privacy constraints in wireless network settings. Differential privacy gives a framework for quantifying the privacy risk of different strategies for compression and data reduction, leading to a privacy-bandwidth-quality tradeoff. At the system level, total privacy risk is a resource that can be managed; this project uses that insight to design novel privacy-allocation schemes. Ultimately, this project seeks to formulate new tradeoffs between privacy and quality-of-service that can generalize to other networking problems. To see how these approaches work in practice, this work involves prototyping and testing the methods in a wireless networking testbed.

该项目研究如何使用隐私来通知未来数据传感系统的设计和管理。收集个人数据的网络系统将在我们的生活中发挥越来越重要的作用，其应用包括工业监测和控制，“智能”家庭/城市和个性化医疗保健。这些系统将收集有关个人的私人信息，这会带来许多耦合的工程挑战。这项工作旨在了解其中两个之间的相互作用：管理必须收集的大量数据和保护系统中个人的隐私。对于依赖于个人群体的应用程序或服务，减少传输的信息量可以节省带宽，同时增强隐私。这项工作的目标是使用数据隐私技术和无线资源管理技术的想法，共同管理隐私和无线传感systems.The技术目标的带宽涉及重新制定分布式数据收集和估计问题的隐私约束下的无线网络设置。差分隐私提供了一个框架，用于量化不同的压缩和数据减少策略的隐私风险，导致隐私带宽质量权衡。在系统层面，总隐私风险是一种可以管理的资源;该项目使用这种洞察力来设计新颖的隐私分配方案。最终，该项目旨在制定隐私和服务质量之间的新的权衡，可以推广到其他网络问题。为了了解这些方法在实践中如何工作，这项工作涉及在无线网络测试平台中对这些方法进行原型设计和测试。