WiMi Hologram Cloud Inc, a leading global Hologram Augmented Reality (“AR”) Technology provider, 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.
Homomorphic 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.
WiMi’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 process can be implemented using secure multi-party computation protocols or differential privacy techniques to ensure data privacy and accuracy of computation results.
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The fusion application of homomorphic encryption and federated learning is of great significance in the data structure, which can provide efficient computation and analysis capabilities while protecting user privacy, bringing more possibilities for technology utilization in the technology industry. This application is expected to play an important role in medical and financial fields, promoting secure data sharing and innovative research, and promoting the continuous development of the big data field.
For example, in the medical field, patients’ medical data often involves personal privacy, and how to share and analyze medical data while ensuring data privacy has been a challenge for medical informatization. WiMi’s architecture provides a feasible solution for secure sharing of medical data by combining federated learning and homomorphic encryption. Hospitals and research institutes can work together to train and optimize medical models without disclosing patients’ personal information, improving the quality and efficiency of medical services. In the financial sector, financial institutions are faced with a large amount of sensitive data, such as customer identity information and transaction records. The leakage of these data may have a serious impact on the reputation of financial institutions. The data structure architecture based on homomorphic encryption and federated learning researched by WiMi can help financial institutions improve the accuracy and efficiency of their risk control models and effectively prevent financial risks by encrypting data sharing and analyzing them under the premise of ensuring data security. In addition, with the popularization of IoT devices and the development of social networks, data generation and sharing have become more and more frequent. How to realize the effective integration and utilization of data while protecting personal privacy has become an urgent problem in these fields. The data structure architecture based on homomorphic encryption and federated learning provides an effective solution to these problems.
SOURCE: Businesswire