MDMS: Efficient and Privacy-Preserving Multi-Dimension and Multi-Subset Data Collection for AMI Networks
Computing Sciences and Computer Engineering
Advanced Metering Infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers’ activities. Privacy-preserving data aggregation techniques have been used to preserve consumers’ privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multi-dimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multi-subset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this paper, we propose an efficient and privacy-preserving multi-dimensional and multi-subset data collection scheme, named “MDMS”. In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks’ gateways using the encrypted readings and following dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers’ readings.
IEEE Internet of Things Journal
(2019). MDMS: Efficient and Privacy-Preserving Multi-Dimension and Multi-Subset Data Collection for AMI Networks. IEEE Internet of Things Journal.
Available at: https://aquila.usm.edu/fac_pubs/16675