Privacy preserving machine learning in energy services: a survey

Privacy preserving machine learning (PPML), which focuses on training machine learning models while preserving privacy, has attracted many research interests in the recent past. This paper investigates recent research on privacy preserving machine learning (PPML) techniques and their energy applications. PPML allows training models while protecting sensitive data. We categorize approaches into federated learning, homomorphic encryption, differential privacy, and secure multi-party computation. After explaining each method's principles, we summarize research applying them to energy tasks like forecasting and anomaly detection. Comparative analysis identifies trade-offs between privacy guarantees, costs, and accuracy. Encryption-based techniques provide strong privacy but impact efficiency. Federated learning is more efficient but has weaker protections. Ongoing challenges include the performance of cryptography, calibrating noise, and developing robust aggregation rules. The survey offers a comprehensive reference on the state-of-the-art in PPML for energy, guiding further advancement of practical systems.