HYBRID NEURAL NETWORK MODELS FOR PRIVACY-PRESERVING DATA PROCESSING - (:unkn) unknown (My University, 2025)
In the era of big data, the need to balance efficient data processing with robust privacy safeguards has become increasingly critical. This paper explores Hybrid Neural Network Models as a promising solution for privacy-preserving data processing. By integrating the strengths of traditional neural architectures (such as Convolutional Neural Networks and Recurrent Neural Networks) with privacy-enhancing technologies like differential privacy, federated learning, and homomorphic encryption, hybrid models can achieve high performance without compromising sensitive information. We discuss architectural designs that optimize both data utility and privacy protection, focusing on adaptive learning mechanisms that minimize data exposure during training and inference. Experimental evaluations across diverse datasets demonstrate that these models maintain competitive accuracy while significantly reducing privacy risks. This research highlights the potential of hybrid neural networks to enable secure, scalable, and efficient data processing in privacy-sensitive domains such as healthcare, finance, and IoT.