  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.

Cryptography and Security