High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumers 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 propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) to protect the privacy of the smart grid data using a split noise cancellation protocol with multiple master smart meters (MSMs) to provide accurate billing and load monitoring and resistance against collusion attacks. 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.

翻译：智能电网中高频上报的能耗数据可能泄露消费者生活方式的敏感信息，构成严重的安全与隐私威胁。基于差分隐私的智能电网隐私模型可在账单计算与负荷监测的能耗数据分析中保障隐私。然而，现有智能电网差分隐私模型易受合谋攻击——攻击者通过与恶意智能电表及不可信聚合器合谋，窃取其他智能电表的隐私信息。我们提出了一种面向智能电表负荷监测与账单计算的增强型差分隐私噪声消除模型（E-DPNCT），通过基于多个主控智能电表的分裂噪声消除协议，在保护智能电网数据隐私的同时实现精确的账单计算与负荷监测，并具备抗合谋攻击能力。我们将E-DPNCT模型与当前最先进的抗攻击隐私保护模型（如EPIC）进行合谋攻击对比实验，基于实时数据的仿真结果表明，该模型在隐私攻击场景下具有显著性能提升。进一步，我们分析了校准差分隐私噪声的不同敏感度参数对用户用电轮廓隐私保护以及负荷监测、账单计算等电力数据聚合准确性的影响。