Machine learning algorithm for multi-modal image segmentation is extensively employed in medical analysis and diagnosis. Clustering represents a mainstream approach for image segmentation, with the fuzzy c-means and particle swarm optimization (FCM-PSO) algorithm garnering significant attention. As image segmentation tasks have substantial computational costs, the outsourcing scheme offers an effective solution by leveraging cloud servers to execute complex computations. Given that medical images contain sensitive patient information, the image segmentation outsourcing scheme must ensure data privacy and confidentiality. In this paper, we propose a secure outsourcing scheme for the FCM-PSO based image segmentation algorithm through a novel sparse matrix encryption method. By analyzing each stage of the image segmentation algorithm, we delegate the computationally intensive task of calculating the Euclidean distance to an untrusted cloud server. We utilize sparse matrices to obscure the private image data. These matrices are created by incorporating multiple small-sized random invertible matrices, thereby circumventing the local storage of generation factors. Additionally, we implement a lightweight verification method to verify the correctness of returned results. Experimental results show that our scheme improves the efficiency of the image segmentation task by 29.99% to 49.50% with the increasing of image set, compared to the original algorithm executed locally.

In this work, we propose a privacy-preserving face recognition scheme based on secure similarity comparison on encrypted data. We innovatively split sensitive face embeddings into two shares and encrypt them to guarantee data privacy. We present a novel matrix blinding method to conduct face similarity computation on encrypted face embeddings. Furthermore, we design an effective re-encryption method to achieve non-local secure data update, which reduces the risk of data leakage. Simulation experiments demonstrate that the proposed scheme completes face recognition tasks securely and efficiently. With different size of datasets, scale of embeddings, and number of queries, our scheme accomplishes face recognition tasks at low costs without obvious accuracy penalty.

In the Internet of Things (IoT) scenarios, edge computing assists in completing machine learning on resource-constrained terminal devices. As one of the most significant operations, large-scale matrix multiplication remains a huge efficiency bottleneck. Existing distributed computation approaches typically decompose the matrix computation into subtasks of the same scale, overlooking edge computing environments with unbalanced computing resources. In this paper, we propose a secure distributed matrix multiplication outsourcing scheme in edge computing with unbalanced resources. Specifically, the large-scale matrix multiplication is decomposed into several subtasks of varying scales according to unbalanced edge computing resources, which achieves better distributed computational performance. A novel matrix blinding method is presented by employing perturbation matrices and permutation matrices to guarantee input and output privacy. Experimental results demonstrate that our scheme improves the efficiency by 51.13% to 98.79% compared to traditional matrix multiplication without outsourcing. Additionally, our scheme outperforms state-of-the-art outsourcing schemes, with an average improvement of 9.36% in edge computing with balanced resources and 14.83% in unbalanced environments.

For the huge amount of data from the Internet of Things (IoT) devices, multiple kernel learning is a widely concerned issue in data analyzing, among which the multiple kernel fuzzy clustering (MKFC) algorithm is an effective approach for extracting linear features in high-dimensional space. For a time-consuming multiple kernel clustering task, it is meaningful to find a secure and efficient outsourcing scheme in the edge-end collaborative architecture, which utilizes edge computing resources while resisting untrusted edge servers. However, existing secure outsourcing schemes cannot align well with the distributed and real-time characteristics of edge computing due to their complex encryption processes. In this article, we propose a secure MKFC outsourcing scheme based on a novel matrix blinding method. The proposed novel matrix blinding method conducts two related encryption operations with disturbance terms, which avoids specific disturbance elimination computations, to reduce the computational burdens in the decryption phase. Additionally, we introduce a sampling verification method to detect the server’s deceptive behaviors. The theoretical analysis demonstrates that our scheme guarantees data privacy and has the capability to verify incorrect results. The experimental results indicate that our scheme is 6.73% superior to other schemes on average when conducting matrix outsourcing computation and enhances the efficiency of conducting the MKFC algorithm by 10.44% to 55.70% on different datasets.