Incentive Mechanism Design for Privacy-Aware Energy Theft Detection Based on Contract Theory

Energy theft remains a major source of non-technical losses in smart grids, leading to significant economic damage and operational risks. Traditional detection methods often rely on fine-grained user consumption data, raising serious privacy concerns and limiting user willingness to cooperate. To address this conflict between detection accuracy and privacy protection, this paper proposes a novel incentive mechanism based on contract theory. We first quantify privacy-preserving levels using the differential privacy framework and analyze the resulting degradation in energy theft detection performance due to added noise. Then, we design an optimal contract menu that encourages users to report more accurate data by compensating them for privacy losses, while accounting for information asymmetry regarding individual privacy preferences. The proposed mechanism is analyzed under both discrete and continuous user types, and the optimization problem is simplified by reducing the number of incentive compatibility and individual rationality constraints. Simulation results demonstrate the feasibility of the proposed mechanism and show how it balances detection performance, aggregation accuracy, and user privacy. This work offers a theoretically grounded and feasible solution to the privacy–detection trade-off in smart grid energy theft detection.