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