Secret-state addition computing device and system for federal learning and privacy computing

Abstract

The invention relates to a secret-state addition computing device and system for federal learning and privacy computing. The computing device comprises at least one layer of Montgomery domain modular multiplication operation module which is configured to perform Montgomery operation and Montgomery domain modular multiplication operation on a plurality of cipher texts in the same batch and output Montgomery domain modular multiplication operation results of the cipher texts. The at least one layer of Montgomery domain modular multiplication operation module comprises a first layer of Montgomery domain modular multiplication operation module, and the first layer of Montgomery domain modular multiplication operation module comprises a plurality of parallel modular multiplication calculation engines and a first layer data distribution module connected with the modular multiplication calculation engines. The first layer data distribution module is configured to send one of the multiple cipher texts every time and select one of the multiple modular multiplication calculation engines in a polling mode to receive the sent cipher texts, and is further configured to generate batch completion signals after all the cipher texts are sent and send the batch completion signals to each of the multiple modular multiplication calculation engines. Therefore, the resource utilization efficiency and the parallel computing speed are improved.

Description

technical field [0001] The present application relates to the technical field of data security and privacy protection, and in particular to a device and system for encrypted state addition computing for federated learning and privacy computing. Background technique [0002] With the development of application fields such as artificial intelligence and big data mining analysis, the demand for data volume is increasing. For example, training artificial intelligence application models requires a large amount of training data with appropriate data labels or feature values. High-quality data often come from the application data generated and accumulated in business activities. However, application data is often scattered in the hands of different organizations and individuals. For example, transaction data is scattered in various financial institutions, and medical diagnosis data is scattered in various medical institutions. Cross-industry and cross-field application data are o...

AI Technical Summary

Problems solved by technology

With the importance of data ownership, user privacy, and data security receiving more attention, and as laws and regulations impose stricter constraints and requirements on data collection and processing, organizations or individuals who control application data are often unwilling or not suitable means to cooperate with each other, making it difficult for the application data held by each to work together

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