| Title | Privacy-Preserving HE-Based Clustering for Load Profiling over Encrypted Smart Meter Data |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Yang, Haomiao, Liang, Shaopeng, Zhou, Qixian, Li, Hongwei |
| Conference Name | ICC 2020 - 2020 IEEE International Conference on Communications (ICC) |
| Keywords | cloud computing, clustering, Collaboration, composability, data privacy, Encryption, homomorphic encryption, Human Behavior, load profiling, Meters, Metrics, Policy-Governed Secure Collaboration, privacy, pubcrawl, resilience, Resiliency, Scalability, smart grid consumer privacy, smart meters |
| Abstract | Load profiling is to cluster power consumption data to generate load patterns showing typical behaviors of consumers, and thus it has enormous potential applications in smart grid. However, short-interval readings would generate massive smart meter data. Although cloud computing provides an excellent choice to analyze such big data, it also brings significant privacy concerns since the cloud is not fully trustworthy. In this paper, based on a modified vector homomorphic encryption (VHE), we propose a privacy-preserving and outsourced k-means clustering scheme (PPOk M) for secure load profiling over encrypted meter data. In particular, we design a similarity-measuring method that effectively and non-interactively performs encrypted distance metrics. Besides, we present an integrity verification technique to detect the sloppy cloud server, which intends to stop iterations early to save computational cost. In addition, extensive experiments and analysis show that PPOk M achieves high accuracy and performance while preserving convergence and privacy. |
| DOI | 10.1109/ICC40277.2020.9148669 |
| Citation Key | yang_privacy-preserving_2020 |
Privacy-Preserving HE-Based Clustering for Load Profiling over Encrypted Smart Meter Data