The amount of data handled by Internet of Medical Things (IoMT) devices grows exponentially, which means higher exposure of sensitive data. The security and privacy of the data collected from IoMT devices, either during their transmission to a cloud or while stored in a cloud, are major unresolved matters. Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis, but most of traditional HEp-2 cell classification algorithms dramatically rely on a single modal feature or fuse different modality features based on fixed weighted schemes, with the result that the complementary information of multimodal features will be not reasonably utilized. In this paper, a class consistent and joint group sparse representation model (CCJGSR) is proposed, expresses the test data through the sparse linear combination of training data and constrains the observations from different modalities of the test object to share their sparse statements. Group sparse representation can fully explore the complementary relationships among different modality features. At the same time, the objective function embeds both the group regularization terms and class consistent, where they enforce the intuitive constraint which the predicted class labels are consistent across all modalities. The experimental results on the HEp2 cell dataset indicate that our proposed algorithm is robust and efficient, and it outperforms existing approaches.

医用物联网（IoMT）设备处理的数据量呈指数增长，这意味着敏感数据的暴露程度更高。从IoMT设备收集的数据在传输到云期间或存储在云中时，其安全性和私密性是未解决的主要问题。自动化的人类喉癌（HEp-2）细胞分类对于医学诊断至关重要，但是大多数传统的HEp-2细胞分类算法极大地依赖于单个模态特征或基于固定加权方案融合不同的模态特征，结果是多模式特征的补充信息将无法合理利用。本文提出了一个类一致和联合组稀疏表示模型（CCJGSR提出），通过训练数据的稀疏线性组合表示测试数据，并约束来自测试对象不同形式的观察结果以共享其稀疏语句。群体稀疏表示可以充分探索不同模态特征之间的互补关系。同时，目标函数嵌入了组正则项和类一致性，它们在其中强制执行了直观的约束，即所预测的类标签在所有模态中都是一致的。在HEp2细胞数据集上的实验结果表明，我们提出的算法是鲁棒且高效的，并且优于现有方法。