A Privacy-Preserving Scheme for Smart Grid Using Trusted Execution Environment

Abstract

The increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer’s residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.