Enhancing Privacy in Real-Time Stream Processing: Federated Transfer Learning Approaches

In the era of data ubiquity, safeguarding privacy while harnessing the power of real-time streaming presents a critical challenge. This study delves into the intersection of privacy preservation, real-time streaming, and federated transfer learning to devise robust solutions. First, we elucidate the essence of data privacy, real-time streaming, and federated transfer learning. Subsequently, we propose an innovative approach that seamlessly integrates privacy considerations into the fabric of real-time streaming analytics through federated learning models. Leveraging this framework, we ascertain the optimal model selection process tailored to the specific data characteristics and user requirements. Our analysis encompasses a comparative evaluation of five prominent models: Differential Privacy, Secure Multi-party Computation, Homomorphic Encryption, Federated Learning with User-level Differential Privacy, and Decentralized Federated Learning. Through empirical examination, we discern the most suitable model for preserving privacy in real-time streaming contexts, thereby advancing the discourse on privacy-preserving machine learning paradigms.