Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data
This addresses privacy-utility tradeoffs for energy systems using synthetic data, but it is incremental as it compares existing methods without introducing new ones.
The study tackled the problem of privacy concerns in using real electricity consumption data for dynamic Time-of-Use tariffs by evaluating four synthetic data generation methods, finding that diffusion models achieved the highest utility with a macro-F1 up to 88.2%, while CTGAN provided the strongest resistance to reconstruction attacks.
The widespread adoption of dynamic Time-of-Use (dToU) electricity tariffs requires accurately identifying households that would benefit from such pricing structures. However, the use of real consumption data poses serious privacy concerns, motivating the adoption of synthetic alternatives. In this study, we conduct a comparative evaluation of four synthetic data generation methods, Wasserstein-GP Generative Adversarial Networks (WGAN), Conditional Tabular GAN (CTGAN), Diffusion Models, and Gaussian noise augmentation, under different synthetic regimes. We assess classification utility, distribution fidelity, and privacy leakage. Our results show that architectural design plays a key role: diffusion models achieve the highest utility (macro-F1 up to 88.2%), while CTGAN provide the strongest resistance to reconstruction attacks. These findings highlight the potential of structured generative models for developing privacy-preserving, data-driven energy systems.