E-DPNCT: An Enhanced Attack Resilient Differential Privacy Model For Smart Grids Using Split Noise Cancellation

TL;DR

This paper introduces E-DPNCT, a novel differential privacy model with split noise cancellation that enhances attack resistance in smart grid data, ensuring privacy without compromising load monitoring and billing accuracy.

Contribution

The paper proposes a new collusion-resistant differential privacy model for smart grids, improving privacy protection against collusion attacks while maintaining data utility.

Findings

E-DPNCT outperforms existing models like EPIC in resisting collusion attacks.

Simulation results show significant privacy enhancement with minimal impact on billing accuracy.

Analysis of sensitivity parameters reveals optimal settings for balancing privacy and data utility.

Abstract

High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer's life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models for smart grids ensure privacy when analysing energy consumption data for billing and load monitoring. However, DP models for smart grids are vulnerable to collusion attack where an adversary colludes with malicious smart meters and un-trusted aggregator in order to get private information from other smart meters. We first show the vulnerability of DP based privacy model for smart grids against collusion attacks to establish the need of a collusion resistant model privacy model. Then, we propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) which not only provides resistance against collusion attacks but also protects the privacy of the smart grid data while providing accurate billing and load monitoring. We use differential privacy with a split noise cancellation protocol with multiple master smart meters (MSMs) to achieve colluison resistance. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collusion attack. We simulate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios. Further, we analyze the impact of selecting different sensitivity parameters for calibrating DP noise over the privacy of customer electricity profile and accuracy of electricity data aggregation such as load monitoring and billing.