Secure and Dropout-Resilient Three-Party Clustering Based on Cloud-Edge-Client Collaboration

Clustering algorithms, as the core technology in data analysis, can extract potential patterns and regularities from complex data. However, deploying k -means clustering on resource-limited devices remains a challenge. Despite the promise of cloud computing, outsourcing data to a remote cloud leads to high latency and privacy risks. Moreover, the stability and speed of cloud can be affected by the state of network and configuration, which leads to computation error. Therefore, we design a secure and dropout-resilient k -means clustering scheme based on cloud-edge-client collaboration architecture. In our scheme, cloud server simply generates multiplication triples in pre-processing phase and can be offline. In online phase, IoT devices secretly share the raw sensing data with three edge servers. Then edge servers accomplish the clustering task interactively. We propose four basic protocols based on vector space secret sharing, including Euclidean distance, comparison, minimum and division protocols. By applying these protocols, we construct a clustering scheme that can tolerate the exit of one edge server and corruption of two edge servers. Since edge servers are generally located in trusted environment, we allow them to reconstruct clustering result and provide low-latency and high-reliability service. We prove that the basic protocols and clustering scheme are secure against semi-honest adversary. We conduct the experiments on two realistic datasets, showing that our scheme has good efficiency and is suitable for practical application.