Privacy-preserving load profile matching for tariff decisions in smart grids

In liberalized energy markets, matching consumption patterns to energy tariffs is desirable, but practically limited due to privacy concerns, both on the side of the consumer and on the side of the utilities. We propose a protocol through which a customer can obtain a better tariff with the help of their smart meter and a third party, based on privacy-preserving load profile matching. Our security analysis shows that the protocol preserves consumer privacy, i.e., neither the load profile nor the matching result are disclosed to the utility, unless the consumer later decides to actually purchase the tariff. In addition, the utility’s load profiles used for matching remain private, allowing each utility to offer special tariffs without disclosing the associated load profiles to their competitors. Our approach is shown to have a smaller ciphertext size than homomorphic encryption in practically relevant configurations. However, matching is only possible with up to about 98 % accuracy in general and 93.5 % based on real-world load profiles, respectively. Depending on the practical requirements, two protocol parameters provide a tradeoff between matching accuracy and ciphertext size. © 2016, The Author(s).