Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations

TL;DR

This paper presents a comprehensive hybrid architecture combining anonymization, privacy-preserving machine learning, synthetic data, and cryptography to enable utility analytics on smart meter data while ensuring compliance with California privacy regulations.

Contribution

It introduces an integrated hybrid framework that combines multiple privacy-preserving techniques for AMI data analytics, tailored to meet strict regulatory privacy requirements.

Findings

The hybrid approach effectively balances privacy and utility in energy data analysis.

Theoretical privacy guarantees are rigorously demonstrated for each technique.

The architecture supports diverse analytics, including forecasting and econometrics, under privacy constraints.

Abstract

Advanced Metering Infrastructure (AMI) data from smart electric and gas meters enables valuable insights for utilities and consumers, but also raises significant privacy concerns. In California, regulatory decisions (CPUC D.11-07-056 and D.11-08-045) mandate strict privacy protections for customer energy usage data, guided by the Fair Information Practice Principles (FIPPs). We comprehensively explore solutions drawn from data anonymization, privacy-preserving machine learning (differential privacy and federated learning), synthetic data generation, and cryptographic techniques (secure multiparty computation, homomorphic encryption). This allows advanced analytics, including machine learning models, statistical and econometric analysis on energy consumption data, to be performed without compromising individual privacy. We evaluate each technique's theoretical foundations, effectiveness, and trade-offs in the context of utility data analytics, and we propose an integrated architecture that combines these methods to meet real-world needs. The proposed hybrid architecture is designed to ensure compliance with California's privacy rules and FIPPs while enabling useful analytics, from forecasting and personalized insights to academic research and econometrics, while strictly protecting individual privacy. Mathematical definitions and derivations are provided where appropriate to demonstrate privacy guarantees and utility implications rigorously. We include comparative evaluations of the techniques, an architecture diagram, and flowcharts to illustrate how they work together in practice. The result is a blueprint for utility data scientists and engineers to implement privacy-by-design in AMI data handling, supporting both data-driven innovation and strict regulatory compliance.