PIPC: Privacy- and Integrity-Preserving Clustering Analysis for Load Profiling in Smart Grids

Generally, power utilities can utilize smart-meter data to extract load patterns through load-profiling technologies, such as $K$ -means clustering. To improve the efficiency of load profiling, both $K$ -means clustering and smart-meter data can be outsourced to powerful clouds. However, clouds are not completely trustworthy: private meter data may be used for commercial interests; $K$ -means clustering may also be performed with fewer iterations to save computational costs, which violates the integrity of outsourced clustering. In this article, therefore, a secure $K$ -means-clustering scheme is proposed, called privacy-preserving and integrity-preserving clustering (PIPC), which aims to protect the privacy and integrity of load profiling. To this end, two techniques are designed: 1) encrypted distance measurement, in which a public comparison matrix is constructed by securely embedding a secret key matrix and 2) integrity assurance, in which a specific Stackelberg game is designed to create economic incentives. The former, as the core of $K$ -means clustering, can protect the privacy of meter data. The latter ensures that clouds can obtain the maximum utility only when clouds execute $K$ -means clustering in an honest manner, thereby preserving the integrity of outsourced computing. Experimental results demonstrate that PIPC reaches high clustering accuracy and computational efficiency for load profiling while retaining smart-meter data privacy and outsourced-clustering integrity.