Abstract

The smart grid is an outcome of integrating communication technologies with traditional electrical systems. This enables the collection of granular metering data from the customer domain for providing grid and billing functionalities. However, the data collection process exposes the grid to various cyberattacks, posing a significant threat to customer privacy. This is a critical concern for the smart grid community and has hindered the global adoption of the smart grid technology. Although aggregation-based frameworks show promise for sharing metering data with the Electrical Service Provider while maintaining customer privacy, existing aggregation-based frameworks have several limitations. Some of these limitations include a high computational overhead on resource-constrained smart meters, susceptibility to single points of compromise due to dependency on a centralized entity, lack of support for dynamic billing functionality, and the absence of integrity verification capabilities for spatial and temporal metering data. To address the aforementioned limitations, we propose a distributed privacy-preserving framework for the smart grid that utilizes secret sharing, commitments, and secure multiparty computation. The framework consists of smart meters employing secret sharing and commitments to outsource their data to multiple aggregating entities, known as Dedicated Aggregators. These Dedicated Aggregators utilize secure multiparty computation to perform spatial aggregation in a privacy-preserving manner and report the aggregated readings to the Electrical Service Provider. By offloading most computations to the Dedicated Aggregators, our framework ensures that it remains lightweight for the smart meters. The introduction of multiple Dedicated Aggregators also aids in mitigating concerns associated with single points of compromise. Additionally, we have adapted the framework to support temporal aggregation, enabling dynamic billing functionalities while preserving customer privacy. The temporal aggregation process is integrated with the spatial aggregation process, thus imposing no additional computational overhead on the smart meters. The framework is designed to cater to both semi-honest and malicious adversarial settings, and works even in the presence of a majority of dishonest Dedicated Aggregators. In the event that some Dedicated Aggregators deviate from the normal execution of computing spatial and/or temporal aggregation by making modifications to metering data, the Electrical Service Provider can detect and respond to such modifications in a privacy-preserving manner. This dissertation presents our proposed framework and conducts a comprehensive analysis of it under different configurations. We develop a proof of concept to illustrate the practicality of implementing our framework in a real-world setting. We also compare its performance with other related works in the literature, evaluating the end-to-end delay for spatial aggregation. Additionally, we analyze the computational overhead on the smart meters in an embedded environment for various framework designs. The resilience of our proposed framework is analyzed against security and privacy threats. Finally, we identify future research directions to extend the capabilities of our framework.

Library of Congress Subject Headings

Smart grids--Security measures; Privacy

Publication Date

7-2023

Document Type

Dissertation

Student Type

Graduate

Degree Name

Computing and Information Sciences (Ph.D.)

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Sumita Mishra

Advisor/Committee Member

Stanislaw Radziszowski

Advisor/Committee Member

Anurag Agarwal

Campus

RIT – Main Campus

Plan Codes

COMPIS-PHD

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