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In this paper, we design and present a novel model called SePEnTra to ensurethe security and privacy of energy data while sharing with other entitiesduring energy trading to determine optimal price signals. Furthermore, themarket operator can use this data to detect malicious activities of users inthe later stage without violating privacy (e.g., deviation of actual energygeneration/consumption from forecast beyond a threshold). We use twocryptographic primitives, additive secret sharing and Pedersen commitment, inSePEnTra. The performance of our model is evaluated theoretically andnumerically. We compare the performance of SePEnTra with the same Transactiveenergy market (TEM) framework without security mechanisms. The result showsthat even though using advanced cryptographic primitives in a large marketframework, SePEnTra has very low computational complexity and communicationoverhead. Moreover, it is storage efficient for all parties.

SePEnTra: A secure and privacy-preserving energy trading mechanisms in transactive energy market