Efficient privacy-preserving outsourcing of imbalanced clustering in cloud computing

Abstract

Imbalanced clustering algorithm plays a vital role in fields, such as fault detection in finance, network security and medical diagnosis. The Imbalanced Clustering with Theoretical Learning Bounds (ICTLB) algorithm is a novel imbalanced clustering algorithm but could incur high computational costs due to extensive matrix operations, making it less practical for resource-limited devices. Outsourcing computations to cloud servers can alleviate client burdens but need to solve data privacy issues and result verification problem. In this paper, we propose an efficient, secure, and verifiable outsourcing scheme for the ICTLB imbalanced clustering algorithm. We design a novel encryption method based on sparse matrices and random permutations, which effectively protects the privacy of the input data while ensuring minimal computational overhead on the client side. Our scheme also integrates a robust verification mechanism, allowing the client to validate the correctness of results returned by the cloud server. Experiments show that the proposed scheme can improve efficiency by 28.88% to 52.48% comparable to the original ICTLB algorithm across various datasets.