Emerging Technologies for Privacy Preservation in Energy Systems

This landscape paper explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating distributed energy resources and other edge-level technologies such as Electric Vehicles or Advanced Metering Infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves toward a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies — Homomorphic Encryption, Secure Multiparty Computation, Differential Privacy, and Federated Learning — as potential tools to improve privacy in the energy domain. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies (PETs) to comply with strict data protection regulations, foster consumer trust, and enhance the security of current and future grid applications. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complex landscape.