| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | May 18, 2015 |
| Latest Amendment Date: | June 21, 2018 |
| Award Number: | 1505422 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Shannon Beck
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $486,524.00 |
| Total Awarded Amount to Date: | $502,524.00 |
| Funds Obligated to Date: |
FY 2018 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
70 Butterfield Terrace Amherst MA US 01003-9284 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CNS, CPS-Cyber-Physical Systems, Secure &Trustworthy Cyberspace |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Smart homes and buildings now include the deployment of numerous Internet-connected metering, sensing, actuation devices that optimize electricity consumption, which collect fine-grained data on energy usage. Analyzing this data can leak unintended private information, such as occupancy and appliance energy usage, that may be used for malice or profit. Thus, ensuring the privacy of such data is critical to both the broader adoption of smart Internet of Things devices and their use as part of a larger smart grid to optimize energy usage. To address the problem, this project both identified novel privacy threats on energy data, and developed novel methods for preserving privacy to mitigate these threats, while preserving the utility of data for non-private analytics necessary for system operation. The project first identified a range of potential privacy threats through energy data, including a novel new privacy threat based on localization, which enable third-parties to identify the precise location of anonymized energy consumption or generation data. The project then developed techniques for mitigating privacy threats with a focus on preserving the utility of data and minimizing cost. In particular, the project developed a new class utility-preserving privacy techniques that obfuscate energy data to prevent leaking sensitive private information (e.g., occupancy), while retaining the ability to perform non-sensitive energy analytics (e.g., disaggregation). Thus, utility-preserving privacy enables users to control their data's level of privacy. Both the privacy attacks and defenses above were evaluated on real-world energy data at large scales. Broader impacts of the project included integration of data privacy into a graduate Green Computing course, and related course projects. The project also provided opportunities for undergraduate research to two students through summer REU research projects. A supported graduate student and REU student were members of under-represented groups, which enhances the diversity of the field. Two doctoral students supported by this project accepted tenure-track faculty positions.
Last Modified: 12/10/2019
Modified by: David Irwin
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