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The purpose of the paper is to provide a look into the lessons learned through the roll-out of the U.S. National Institute of Standards and Technology and U.S. Department of Energy Green Button electricity, natural gas, and water data-access initiative: to free-up energy and water consumption data for consumers and third-party companies assisting mutual customers of utilities; all while protecting the security and privacy of the data. Energy and water usage data are important for smart cities. Smart-city solutions rely heavily on the availability of data in order to provide situational awareness as well as to inform control, actuation, and decision making. However, the data need to be protected both for security and integrity. Bad data are worse than no data at all. We would like to present a case study in Green Button and the steps we took to ensure data security and privacy while enabling access to those consumption data by the consumer and third parties. We were able to achieve the data security and privacy through the Green Button standard and subsequent implementation by the Green Button Alliance of a compliance- testing program. Considerations and solutions were needed for data in transit, data at rest, and the authorization mechanisms for allowing unregulated third-party companies to interface directly to utilities on behalf of the consumer while ensuring the consumer maintains complete control of what is to be shared and the ability to revoke that sharing at any time. The lessons learned from this approach could be applicable to other smart-city data.
Proceedings Title
Proceedings of The 2nd ACM/EIGSCC Symposium On Smart Cities and Communities
Conference Dates
September 10-12, 2019
Conference Location
Portland, OR, US
Conference Title
The 2nd ACM/EIGSCC Symposium On Smart Cities and Communities
Nguyen, C.
and Roberts, J.
(2019),
Green Button Data-Access Model for Smart Cities, Proceedings of The 2nd ACM/EIGSCC Symposium On Smart Cities and Communities, Portland, OR, US, [online], https://doi.org/10.1145/3357492.3358624, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928549
(Accessed December 21, 2024)