Large-scale secure model learning and inference using synthetic data for IoT-based big data analytics

Abstract

Big data analytics in the Internet of Things (IoT) realm demands a substantial volume of data for training models and making reliable inferences. In most cases, data availability is scarce, and synthetic data is generated from real-world data to meet the needs. Yet, there remains a risk of exposing private and sensitive information without proper data security measures. In this article, we aim to develop a secure collaborative model learning methodology trained on synthetic data, ensuring data availability, privacy and confidentiality through differential privacy and key management. Additionally, we propose a secured inference framework where user data, sent for inference to the deployed model is protected, preserving both the accuracy of the predicted data and the security of the input data. Our experimental evaluation, along with performance and security analysis, exhibits that our approach offers accuracy and scalability while maintaining privacy and security.