WiMi Built an Advanced Data Structure Architecture Using Homomorphic Encryption and Federated Learning

About this update from Wimi Hologram Cloud Inc.

[{"type":"text","content":"BEIJING, July 26, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced that it utilized homomorphic encryption and federated learning building an advanced data structure architecture. The architecture integrates federated learning and partial homomorphic encryption, and this integration protects data privacy while enabling efficient data analysis and sharing.\n\nHomomorphic encryption is a special encryption technique that enables computational operations to be performed in an encrypted state without decrypting the data. By utilizing homomorphic encryption, it is possible to compute and share data in an encrypted state while protecting data privacy and integrity, which is useful for some scenarios involving sensitive data. Federated learning is a distributed machine learning technique that enables model improvement by allowing multiple participants to train models on their respective local datasets without sharing the original data, and aggregating the learned parameters of these models into a global model. In data structuring, federated learning can address the issues of data privacy and data security.\nWiMi's data structure architecture based on homomorphic encryption and federated learning enables data collaboration, sharing and integration without revealing the original data content. Participants can train models and update parameters without direct access to the original data of other participants, providing an effective and reliable data fusion solution for secure sharing and analysis of big data. The architecture not only protects the privacy of data, but also improves the efficiency and accuracy of data integration. In practical application, firstly, the requirements of the data architecture need to be analyzed in detail, including data type, data size, and computational tasks. Based on the results of the demand analysis, the design objectives and functions of the data structure are determined. Then, homomorphic encryption technology is utilized to encrypt the user's sensitive data to ensure that the data remains encrypted during the computation process. The encrypted data from the participating parties are then aggregated and computed using federated learning techniques. The federated learning...

More updates from Wimi Hologram Cloud Inc.