Privacy-Preserving kNN Classification Query Scheme for Encrypted Data in Outsourced Environments for Smart Grid

With the increasing application of cloud computing in the power system, numerous power companies outsource their data to cloud service providers to reduce data management costs. However, there are many privacy issues. Privacy-preserving kNN classification queries for power data stand out as a typical application of privacy computing tech-nology in smart grid environments. Nevertheless, numerous kNN classification query solutions still face challenges such as security vulnerabilities, inapplicability to data outsourcing scenarios, and low computational efficiency. To address these issues, we introduced the SKL, SKS, and SCF protocols, laying the foundation for a privacy-preserving kNN classification query scheme for encrypted data in outsourced environments for the smart grid. Furthermore, we proved the security of our scheme under the semi-honest security model. Lastly, empirical validation attests to the high computational efficiency of our proposed scheme.