mlsanityData to 14 Oct 2026

Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data

arXiv 2506.11026 ↗First posted 20 May 2025cs.LG cs.CYcited by 0

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Abstract (arXiv)

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.

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References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2017Improved Training of Wasserstein GANscited by 2,235
  7. 2014Conditional Generative Adversarial Netscited by 2,162
  8. 2019Modeling Tabular data using Conditional GANcited by 699
  9. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  10. 2025Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesiscited by 10
  11. 2022An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Modelscited by 6
  12. 2024Faraday: Synthetic Smart Meter Generator for the smart gridcited by 6
4 more
  1. 2024Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Methodcited by 3
  2. 2020GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issuecited by 2
  3. 2017Electricity Theft Detection using Machine Learningcited by 1
  4. 2024Privacy Re-identification Attacks on Tabular GANscited by 1

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  1. 2024Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
  2. 2025A synthetic dataset of French electric load curves with temperature conditioning
  3. 2025Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
  4. 2024Comprehensive Exploration of Synthetic Data Generation: A Survey
  5. 2025SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
  6. 2024Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records
  7. 2023Generating tabular datasets under differential privacy
  8. 2024DP-TLDM: Differentially Private Tabular Latent Diffusion Model