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Big Data Privacy Preservation for Cyber-Physical SystemsOptimization for Utility Providers with Privacy Preservation of Users’ Energy Profile

Big Data Privacy Preservation for Cyber-Physical Systems: Optimization for Utility Providers with... [Smart meters migrate conventional electricity grid into digitally enabled SG, which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users’ demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters”. To enjoy the benefits of smart meter measured data without compromising the users’ privacy, in this chapter, we try to integrate DDP techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users’ energy profiles. Briefly, we add differential private noises to the users’ energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users’ demand distribution, the utility provider aggregates a given set of historical users’ differentially private data, estimates the users’ demands, and formulates the data-driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company’s real data analysis.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Big Data Privacy Preservation for Cyber-Physical SystemsOptimization for Utility Providers with Privacy Preservation of Users’ Energy Profile

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
ISBN
978-3-030-13369-6
Pages
35 –45
DOI
10.1007/978-3-030-13370-2_4
Publisher site
See Chapter on Publisher Site

Abstract

[Smart meters migrate conventional electricity grid into digitally enabled SG, which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users’ demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters”. To enjoy the benefits of smart meter measured data without compromising the users’ privacy, in this chapter, we try to integrate DDP techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users’ energy profiles. Briefly, we add differential private noises to the users’ energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users’ demand distribution, the utility provider aggregates a given set of historical users’ differentially private data, estimates the users’ demands, and formulates the data-driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company’s real data analysis.]

Published: Mar 26, 2019

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