Enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions

This paper meticulously examines the privacy challenges stemming from the escalating volume of data within the new power systems. It thoroughly explores various privacy computing techniques, including encryption and statistical security, with a particular emphasis on applications such as homomorphic encryption, federated learning and differential privacy within the power grid. The analysis underscores the pivotal role of privacy computing in addressing privacy-related concerns within the power industry, particularly focusing on issues like data transmission security and multi-party collaboration. Furthermore, the paper proposes innovative solutions aimed at bolstering privacy protection, fostering data sharing initiatives, and enhancing cost-effectiveness through the strategic implementation of privacy computing technologies. Recognizing the paramount importance of understanding privacy protection in the context of the digital transformation of power systems, this paper seeks to provide valuable insights to guide further research and application of privacy computing. Ultimately, these insights aim to facilitate secure data circulation within the energy sector.