Efficient and Privacy Preserving User Data Aggregation and Billing Scheme for Smart Grid

Abstract

Smart Grid is an electrical power supply infrastructure that exploits communication technology to detect and react to changes in demand and supply. A smart grid's main objective is to maximize the use of electrical power by utilizing Realtime interaction between the user side and the generation side. Smart Meters (SM) are an essential component of smart grids, giving residential customers the ability to track and manage their energy costs. A SM today is capable of collection of real-time information on household electricity use. The immense volume of data generated by SM’s can be monitored and controlled in real time by utility companies to achieve operational accuracy. Therefore, the data collected by SMs meets utility-privacy tradeoff. On one side, utility providers require customer data in order to precisely and flexibly control household energy, i.e., the Control Center (CC) can provide electric power during the peak periods of electric use and can and control the charging of storage devices during periods of low demand. With fine-grained data, CC can also identify illegal users and can predict the electrical load. In addition, certain service providers may require customer data in order to enable smart home automation. SMs, on the other hand, lack security features that safeguard the confidentiality, integrity, authenticity, and privacy of user data. They collect fine grained energy usage data, which can compromise users' privacy, especially because the data is collected on a much larger scale, more frequently, and in a detailed manner. The fine-grained metering data could be used by an intruder to learn the consumer's identity and track his/her daily activities. Using fine grained consumption-data, malicious attackers can infer human activity inside a house. Hence, the tradeoff between user privacy and data usability becomes a crucial issue. One of the biggest challenges in smart grid’s research is to maintain user privacy while maximizing Data Utility along with proper Billing, as sharing user data can enable internal and external adversaries to learn about user habits and behaviors. In this research, we have proposed a secure, privacy-preserving mechanism that fulfils various security requirements, ensures maximum utility and accuracy in billing. Utilizing low communication and computation costs, the scheme guarantees the user’s privacy, while protecting the network on the customer's side from multiple types of attacks.