Towards Efficient and Privacy-Preserving Cloud Computing

Cloud computing offers a scalable infrastructure that gives the impression of virtually unlimited computing resources, attracting a wide range of applications. However, it raises significant concerns regarding data privacy and risks user trust due to making the required computation on a remote machine (rather than on a local machine) and transmitting data over communication channels. Homomorphic encryption provides feasible solutions but introduces extra computational costs due to related cryptographic data conversions. So, it is crucial to balance computational efficiency with effective privacy preservation. We propose an intelligent framework capable of classifying text data into private and public categories, thereby enabling context-aware routing of data processing tasks. Specifically, private data is processed locally to safeguard user privacy, while public data is offloaded to powerful cloud servers for resource-intensive analytics. This selective processing strategy using an efficient machine learning strategy seeks to balance privacy, latency, and efficient resource utilization. Our developed framework was tested with a cloud application using Transformer-based models, specifically DistilBERT that shows strong performance, achieving 99.98% accuracy in private-public classification. Finally, DistilBERT was used for a privacy-aware spam detection cloud application with successful integration of federated learning (FL) approach.