As smart grids are getting popular and being widely implemented, preserving the privacy of consumers is becoming more substantial. Power generation and pricing in smart grids depends on the continuously gathered information from the consumers. However, having access to the data relevant to the electricity consumption of each individual consumer is in conflict with its privacy. One common approach for preserving privacy is to aggregate data of different consumers and to use their smart-meters for calculating the bills. But in this approach, malicious consumers who send erroneous data to take advantage or disrupt smart grid cannot be identified. In this paper, we propose a new statistical-based scheme for data gathering and billing in which the privacy of consumers is preserved, and at the same time, if any consumer with erroneous data can be detected. Our simulation results verify these matters.
Abstract In a cyber-physical system, the control component plays an essential role to make the cyber and physical components work harmoniously together. When information collected from the physical space contains private or sensitive data that cannot be passed onto the cyber space, properly controlling the cyber-physical system becomes a very challenging task. For instance, the smart grid systems, a replacement for the traditional power grid systems, have been widely used in the industries. To prevent power shortage, threshold-based power usage control (PUC) in a smart grid considers a situation where the utility company sets a threshold to control the total power usage or supply of a neighborhood. If the total power usage exceeds the threshold, either certain households need to reduce their power consumption or the utility company needs to buy additional power supplies to meet the increasing demand. In these scenarios, the utility company needs to frequently collect power usage data from smart meters. It has been well documented that these power usage data can reveal a person's daily activity and violate personal privacy. To mitigate the privacy concerns, the goal of this paper is to develop efficient and privacy-preserving power usage control protocols that allow a utility company to balance supply and demand in a smart grid without violating personal privacy of its customers. We will provide extensive empirical study to show the practicality of our proposed protocols. Abstract The electric industry's planned shift to smart grid metering infrastructure raised several concerns especially about preserving the privacy. Various data perturbation and aggregation solutions are being developed to address these concerns. The drawback of these solutions is that a simple random noise scheme cannot protect privacy, and there is a need for more advanced perturbation techniques to increase hardware costs of smart metering devices. The proposed data aggregation scheme combines the power of perturbation techniques with crypto-systems in an efficient and lightweight way so that it becomes applicable for devices with limited hardware, such as smart meters. We investigated the privacy preserving capabilities of the proposed aggregation scheme with Holt-Winters and Seasonal Trend Decomposition using Loess prediction methods. The results indicate that the proposed scheme is resilient to both filtering and true value attacks.