K-anonymity applied to the energy grid of things distributed energy resource management system

Alsaid, Mohammed; Slay, Tylor; Bulusu, Nirupama; Bass, R.

doi:10.1145/3498361.3538794

Cited by 2 publications

(1 citation statement)

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“…Alsaid et al. applied the Mondrian algorithm to ensure k-anonymity by excluding personally identifiable information within a smart grid system [ 27 ], which achieves anonymization in

time complexity. Stegelmann and Kesdogan proposed using pseudonyms implementing k-anonymization to avoid the service provider identifying a specific customer in a smart grid [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Sun

Cardell-Oliver

et al. 2023

Sensors

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Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy.

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“…Alsaid et al. applied the Mondrian algorithm to ensure k-anonymity by excluding personally identifiable information within a smart grid system [ 27 ], which achieves anonymization in

time complexity. Stegelmann and Kesdogan proposed using pseudonyms implementing k-anonymization to avoid the service provider identifying a specific customer in a smart grid [ 28 ].…”

Section: Related Workmentioning

confidence: 99%