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High frequency reporting of energy consumption data in smart grids can beused to infer sensitive information regarding the consumer's life style andposes serious security and privacy threats. Differential privacy (DP) basedprivacy models for smart grids ensure privacy when analysing energy consumptiondata for billing and load monitoring. However, DP models for smart grids arevulnerable to collusion attack where an adversary colludes with malicious smartmeters and un-trusted aggregator in order to get private information from othersmart meters. We first show the vulnerability of DP based privacy model forsmart grids against collusion attacks to establish the need of a collusionresistant model privacy model. Then, we propose an Enhanced DifferentialPrivate Noise Cancellation Model for Load Monitoring and Billing for SmartMeters (E-DPNCT) which not only provides resistance against collusion attacksbut also protects the privacy of the smart grid data while providing accuratebilling and load monitoring. We use differential privacy with a split noisecancellation protocol with multiple master smart meters (MSMs) to achievecolluison resistance. We did extensive comparison of our E-DPNCT model withstate of the art attack resistant privacy preserving models such as EPIC forcollusion attack. We simulate our E-DPNCT model with real time data which showssignificant improvement in privacy attack scenarios. Further, we analyze theimpact of selecting different sensitivity parameters for calibrating DP noiseover the privacy of customer electricity profile and accuracy of electricitydata aggregation such as load monitoring and billing.

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