Breakthrough: Enhancing Privacy in Smart Buildings and Homes

突破：增强智能建筑和家庭的隐私

• 批准号: 1505422
• 负责人: David Irwin
• 金额: $ 48.65万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505422&HistoricalAwards=false
• 关键词: Breakthrough; Enhancing; Privacy; Smart; Buildings

The design of smart electric grids and buildings that automatically optimize their energy generation and consumption is critical to advancing important societal goals, including increasing energy-efficiency, improving the grid's reliability, and gaining energy independence. To enable such optimizations, smart grids and buildings increasingly rely on Internet-connected sensors in smart devices, including digital electric meters, web-enabled appliances and lighting, programmable outlets and switches, and intelligent HVAC systems. However, a key barrier to the broad adoption of energy-related optimizations is that prior work has shown that Internet-connected sensors inadvertently leak sensitive private information about user behavior. For example, a high or variable home energy usage typically correlates with a home being occupied. To address the problem, this research will design low-cost, non-intrusive, privacy-enhancing techniques that reduce the sensitive information leaked through smart sensor-driven devices, while still permitting the sophisticated analytics, control, and verification necessary to enable energy optimizations for smart grids and buildings. The research includes developing both consumer- and utility-driven mechanisms to preserve sensor-data privacy. The consumer-driven mechanisms leverage batteries, elastic appliances, noise injection, and renewable energy sources to obfuscate private information in externally visible energy usage data at low cost. The utility-driven mechanisms leverage cryptographic techniques within the devices themselves to enable utilities to implement critical electric grid optimizations, such as demand response, time-of-use billing, and fault localization, without requiring consumers to provide utilities, or other third-parties, with their raw sensor data. The research also develops an approach to controllable privacy, which enables users to control the amount of information smart devices leak to third parties. In this case, consumers voluntarily use smart devices, which are able to verify that consumers engage in some particular energy-efficient behavior without directly revealing sensitive information. The research includes implementing and evaluating the techniques in a prototype programmable building, which includes programmable smart devices, batteries, and renewable energy sources. The research and prototype provide awareness of smart grid privacy and its implications on public policy, and contribute to both graduate courses on smart grids and energy, as well as undergraduate research projects.

智能电网和建筑物的设计，自动优化其能源生产和消费是推进重要的社会目标，包括提高能源效率，提高电网的可靠性，并获得能源独立的关键。为了实现这种优化，智能电网和建筑物越来越依赖智能设备中的互联网连接传感器，包括数字电表、网络家电和照明、可编程插座和开关以及智能HVAC系统。然而，广泛采用能源相关优化的一个关键障碍是，先前的工作表明，连接互联网的传感器会无意中泄露有关用户行为的敏感私人信息。例如，高的或可变的家庭能量使用通常与家庭被占用相关。为了解决这个问题，这项研究将设计低成本，非侵入性，隐私增强技术，减少通过智能传感器驱动设备泄漏的敏感信息，同时仍然允许进行必要的复杂分析，控制和验证，以实现智能电网和建筑物的能源优化。该研究包括开发消费者和公用事业驱动的机制，以保护传感器数据隐私。消费者驱动的机制利用电池、弹性电器、噪声注入和可再生能源，以低成本模糊外部可见的能源使用数据中的私人信息。公用事业驱动的机制利用设备本身中的加密技术，使公用事业能够实现关键的电网优化，例如需求响应，使用时间计费和故障定位，而不需要消费者向公用事业或其他第三方提供原始传感器数据。该研究还开发了一种可控隐私的方法，使用户能够控制智能设备泄露给第三方的信息量。 在这种情况下，消费者自愿使用智能设备，这些设备能够验证消费者参与某些特定的节能行为，而不会直接泄露敏感信息。该研究包括在原型可编程建筑中实施和评估这些技术，其中包括可编程智能设备，电池和可再生能源。该研究和原型提供了对智能电网隐私及其对公共政策的影响的认识，并有助于智能电网和能源的研究生课程以及本科生研究项目。

项目成果

vSolar: Virtualizing Community Solar and Storage for Energy Sharing

vSolar：虚拟化社区太阳能和存储以实现能源共享

• DOI: 10.1145/3208903.3208932
• 发表时间: 2018
• 期刊: ACM International Conference on Future Energy Systems
• 作者: Lee, Stephen;Shenoy, Prashant;Ramamritham, Krithi;Irwin, David
• 通讯作者: Irwin, David

DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery

• DOI: 10.1145/3292500.3330741
• 发表时间: 2019-07
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Stephen Lee;Srinivasan Iyengar;Menghong Feng;Prashant J. Shenoy;Subhransu Maji

Helios: a programmable software-defined solar module

Helios：可编程软件定义太阳能模块

• DOI: 10.1145/3276774.3276783
• 发表时间: 2018
• 期刊: Proceedings of the 5th Conference on Systems for Built Environments
• 作者: Bashir, Noman;Irwin, David;Shenoy, Prashant
• 通讯作者: Shenoy, Prashant

Weatherman: Exposing weather-based privacy threats in big energy data

气象员：揭露大能源数据中基于天气的隐私威胁

• DOI: 10.1109/bigdata.2017.8258032
• 发表时间: 2017
• 期刊: IEEE International Conference on Big Data
• 作者: Chen, Dong;Irwin, David
• 通讯作者: Irwin, David