A survey and design proposal asserting that combining differential privacy, synthetic data, federated learning, secure multiparty computation, and homomorphic encryption can comply with CPUC smart meter privacy rules while preserving analytics utility.
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Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations
A survey and design proposal asserting that combining differential privacy, synthetic data, federated learning, secure multiparty computation, and homomorphic encryption can comply with CPUC smart meter privacy rules while preserving analytics utility.