Smart grid allows fine-grained smart metering data collection which can improve the efficiency and reliability of the grid. Unfortunately, this vast collection of data also impose risks to users' privacy. In this paper, we propose a novel protocol that allows suppliers and grid operators to collect users' aggregate metering data in a secure and privacy-preserving manner. We use secure multiparty computation to ensure privacy protection. In addition, we propose three different data aggregation algorithms that offer different balances between privacy-protection and performance. Our protocol is designed for a realistic scenario in which the data need to be sent to different parties, such as grid operators and suppliers. Furthermore, it facilitates an accurate calculation of transmission, distribution and grid balancing fees in a privacy-preserving manner. We also present a security analysis and a performance evaluation of our protocol based on existing multiparty computation algorithms.

Abstract-This paper proposes a local electricity trading market and provides a comprehensive security analysis of this market. It first presents a market for electricity trading among individual users, and describes the different entities and the interactions among them. Based on this market model and the interactions, the paper analyses security problems and potential privacy threats imposed on users, which leads to the specification of a set of security and privacy requirements. These requirements can be used to guide the future design of secure local electricity trading markets or to perform a risk assessment of such markets.

Abstract. This paper proposes a decentralised and privacy-preserving local electricity trading market. The market employs a bidding protocol based on secure multiparty computation and allows users to trade their excess electricity among themselves. The bid selection and trading price calculation are performed in a decentralised and privacy-preserving manner. We implemented the market in C++ and tested its performance with realistic data sets. Our simulation results show that the market tasks can be performed for 2500 bids in less than four minutes in the "online" phase, showing its feasibility for a typical electricity trading period.