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 in Transactive Energy Market (TEM). A market operator can store and use this data to detect malicious activities (deviation of actual energy generation/consumption from forecast data beyond a threshold) of users in later stages of energy trading without violating privacy. We use two cryptographic primitives; additive secret sharing and Pedersen commitment, in SePEnTra. We analyze the security of our proposed model in this work. The performance of our proposed model is evaluated theoretically and numerically, considering practical TEM scenarios. The result shows that even though using two advanced cryptographic primitives in a large market framework, our proposed model has very low computational complexity and communication overhead. For example, in a TEM with 100 users, SePEnTra achieves an execution time of 0.92 s.