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Exposing the location of anonymous solar-powered homes: poster

Published: 15 May 2019 Publication History

Abstract

Due to the decline of solar module prices, more and more people install solar panel energy systems. To better analyze the solar performance, solar generation data are transmitted on the Internet, stored in the cloud, even making the data available to the public. These data can leak privacy information, such as, the occupancy. However, people believe this information is not useful as the solar energy are "anonymous", which means the data cannot be associated to any identification information, such as account number or address, thus these solar-powered home energy data is often not treated as sensitive.
Our key insight is solar energy data is not anonymous: since every location on the earth has unique solar and weather signature. We design a system to localize the "anonymous" solar-powered homes. We first localize the source home to a small region of interest by inferring the latitude and longitude from the information inherently embedded in the solar data. We then identify solar-powered homes within this region using satellite image processing by extracting and detecting rooftop solar deployment using a hybrid convolution neural networks (CNN) approach to identify a specific home without extra cost.

References

[1]
G. Barbose, S. Weaver, and N. Darghouth. Tracking the Sun VII: An Historical Summary of the Installed Price of Photovoltaics in the United States from 1998 to 2013. Technical report, Lawrence Berkeley National Laboratory, September 2014.
[2]
J. St. John. Bidgely Thinks Algorithms Can Replace Hardware to Capture the Impact of Rooftop Solar. http://www.greentechmedia.com/articles/read/bidgely-launches-solar-disaggregation-for-the-home, July 8th 2014
[3]
W. Kleiminger, C. Beckel, T. Staake, and S. Santini. Occupancy Detection from Electricity Consumption Data. In BuildSys, November 2013.
[4]
D. Chen and D. Irwin, "Weatherman: Exposing weather-based privacy threats in big energy data," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 1079--1086.

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    cover image ACM Conferences
    WiSec '19: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks
    May 2019
    359 pages
    ISBN:9781450367264
    DOI:10.1145/3317549
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

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    Published: 15 May 2019

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    Author Tags

    1. CNN
    2. image processing
    3. privacy
    4. solar modeling

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