Differentially Private Anomaly Detection for Energy Market Data

Differential privacy (DP) is often used to protect consumer privacy in controlled data disclosures. This paper applies DP technology in anomaly detection to safeguard the secrecy of business-sensitive data in Brazilian open energy markets, where full metering datasets are publicly disclosed. Anomaly detection on actual metering data can be found wrong when random noise preserves secrecy, but reduces data utility. This paper contributes to solve this real-world issue by demonstrating DP’s utility for anomaly detection in energy consumption data. We evaluated this approach across various values for the privacy parameter (ϵ), analyzing Precision, Recall, and F1-Score metrics. Our findings facilitate fine-tuning the trade-off between anomaly detection and business secrecy, minimizing the risk of inaccurate insights from noisy data while ensuring robust privacy.