A Novel Approach for Enhancing Privacy and Integrity in Cloud-based Meteorological Data Management

Abstract

The cloud-based meteorological data management offers vast potential for research and collaboration, but inherent privacy concerns and data integrity risks necessitate robust solutions. This research suggests a new, multi-layered method to improve integrity and privacy in this field. By utilizing homomorphic encryption, this study makes it possible to perform calculations on encrypted meteorological data while keeping private information hidden from cloud service providers. Federated learning promotes cooperation without compromising the privacy of raw data by enabling distributed learning on local datasets. Lastly, zero-knowledge proofs are integrated to confirm the accuracy of computations made on encrypted data, ensuring the validity of outcomes without disclosing the underlying data. This integrated strategy presents a viable path for reliable and safe cloud-based meteorological data administration, promoting cutting-edge study and creativity while protecting private data.