Privacy Protection Anomaly Detection in Smart Grids Based on Combined PHE and TFHE Homomorphic Encryption

With the growing scale and complexity of smart grids, ensuring both effective anomaly detection and robust privacy protection has become increasingly critical. This paper proposes a ciphertext-based anomaly detection model built upon a collaborative architecture between edge computing and public cloud, integrating a hybrid homomorphic encryption scheme that combines partial homomorphic encryption (PHE) and fully homomorphic encryption over torus (TFHE). The encryption method is selected based on the task type: TFHE is used for complex anomaly detection tasks requiring encrypted computation in the cloud, while PHE is applied to cross-regional data aggregation tasks for secure homomorphic addition. Edge nodes handle low-latency, lightweight tasks locally, whereas complex encrypted tasks are processed in the cloud using an enhanced Isolation Forest model adapted for homomorphic computation. Extensive experiments on three benchmark datasets demonstrate that the proposed model achieves anomaly detection performance comparable to plaintext-based models, while significantly outperforming existing homomorphic encryption-based methods in terms of accuracy and ROC-AUC. This work provides a scalable and practical solution for secure and efficient anomaly detection in smart grids.