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.

翻译：本文设计并提出了一种名为SePEnTra的创新模型，旨在确保能源数据在交易过程中与其他实体共享时的安全性与隐私性，从而确定最优价格信号。此外，市场运营商可借助这些数据在不侵犯隐私的前提下（例如实际能源发电/消耗与预测值偏差超过阈值）在后续阶段检测用户的恶意行为。SePEnTra采用了两种密码学原语：加法秘密共享与Pedersen承诺。我们通过理论分析与数值仿真对该模型的性能进行了评估，并将SePEnTra与未部署安全机制的同类型交易能源市场（TEM）框架进行了对比。结果表明，尽管在大型市场框架中使用了高级密码学原语，SePEnTra仍具有极低的计算复杂度和通信开销，且对所有参与方均具有存储高效性。