Effectiveness of Privacy Techniques in Smart Metering Systems

Martin Peralta-Peterson; Olivera Kotevska

doi:10.1109/CSCI54926.2021.00177

Effectiveness of Privacy Techniques in Smart Metering Systems

Martin Peralta-Peterson, Olivera Kotevska

Discrete Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and ϵ-differential privacy. We demonstrate the models' effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.

Original language	English
Title of host publication	Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	675-678
Number of pages	4
ISBN (Electronic)	9781665458412
DOIs	https://doi.org/10.1109/CSCI54926.2021.00177
State	Published - 2021
Event	2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 - Las Vegas, United States Duration: Dec 15 2021 → Dec 17 2021

Publication series

Name	Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021

Conference

Conference	2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021
Country/Territory	United States
City	Las Vegas
Period	12/15/21 → 12/17/21

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle,LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725.

Funders	Funder number
U.S. Department of Energy	DE-AC05-00OR22725
Oak Ridge National Laboratory

Keywords

differential privacy
k-anonymity
l-diversity
smart meter
t-closeness

Access to Document

10.1109/CSCI54926.2021.00177

Cite this

Peralta-Peterson, M., & Kotevska, O. (2021). Effectiveness of Privacy Techniques in Smart Metering Systems. In Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 (pp. 675-678). (Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI54926.2021.00177

Peralta-Peterson, Martin ; Kotevska, Olivera. / Effectiveness of Privacy Techniques in Smart Metering Systems. Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 675-678 (Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021).

@inproceedings{77f072c1279c407fa811e9bfad9befb6,

title = "Effectiveness of Privacy Techniques in Smart Metering Systems",

abstract = "Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and ϵ-differential privacy. We demonstrate the models' effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.",

keywords = "differential privacy, k-anonymity, l-diversity, smart meter, t-closeness",

author = "Martin Peralta-Peterson and Olivera Kotevska",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 ; Conference date: 15-12-2021 Through 17-12-2021",

year = "2021",

doi = "10.1109/CSCI54926.2021.00177",

language = "English",

series = "Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "675--678",

booktitle = "Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021",

}

Peralta-Peterson, M & Kotevska, O 2021, Effectiveness of Privacy Techniques in Smart Metering Systems. in Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021. Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021, Institute of Electrical and Electronics Engineers Inc., pp. 675-678, 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021, Las Vegas, United States, 12/15/21. https://doi.org/10.1109/CSCI54926.2021.00177

Effectiveness of Privacy Techniques in Smart Metering Systems. / Peralta-Peterson, Martin; Kotevska, Olivera.
Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 675-678 (Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Effectiveness of Privacy Techniques in Smart Metering Systems

AU - Peralta-Peterson, Martin

AU - Kotevska, Olivera

PY - 2021

Y1 - 2021

N2 - Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and ϵ-differential privacy. We demonstrate the models' effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.

AB - Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and ϵ-differential privacy. We demonstrate the models' effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.

KW - differential privacy

KW - k-anonymity

KW - l-diversity

KW - smart meter

KW - t-closeness

UR - http://www.scopus.com/inward/record.url?scp=85133949499&partnerID=8YFLogxK

U2 - 10.1109/CSCI54926.2021.00177

DO - 10.1109/CSCI54926.2021.00177

M3 - Conference contribution

AN - SCOPUS:85133949499

T3 - Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021

SP - 675

EP - 678

BT - Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021

Y2 - 15 December 2021 through 17 December 2021

ER -

Peralta-Peterson M, Kotevska O. Effectiveness of Privacy Techniques in Smart Metering Systems. In Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 675-678. (Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021). doi: 10.1109/CSCI54926.2021.00177

Effectiveness of Privacy Techniques in Smart Metering Systems

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this