Secure Outsourcing of Deep Active Learning in the IoT: From Both Sample Selection and Model Update Perspectives
Jishu K. Medhi, Xuhui Chen, Miao Pan, Pan Li · IEEE Transactions on Network Science and Engineering · 2025
The Internet of Things (IoT) emerges as a ubiquitous information collection and processing paradigm that can potentially exploit massive data for various applications using machine learning technologies. However, these data are usually unlabeled, and the labeling process is usually both time and effort consuming. While active learning can reduce data labeling costs, the two computation-intensive operations, i.e., sample selection and learning model training, hinder the use of active learning on resource-limited IoT devices. Although cloud computing may relieve users from expensive computations, directly outsourcing data and computations to the cloud raises critical security and privacy concerns. In this paper, we develop a secure outsourcing framework for deep active learning (SEDAL) by considering a general active learning framework with a deep neural network (DNN) learning model. Instead of using computationally expensive encryption based methods, we utilize a lightweight linear algebra based secure matrix transformation scheme to protect the user's input data and the DNN model. Compared to traditional homomorphic encryption based secure outsourcing schemes, our scheme reduces the computational complexity at the user from$O(n^{3})$to$O(n^{2})$. We evaluate the performance by implementing it on an arrhythmia diagnosis application. Experimental results show that the proposed scheme can obtain a well-trained classifier using fewer queried samples, the computation time outperforms the traditional homomorphic encryption and the state-of-the-art privacy-preserving outsourcing scheme (EVPP), and the computation time and communication overheads are acceptable and practical.