A combined deep learning training method based on a privacy protection technology

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to a combined deep learning training method based on a privacy protection technology. The efficient combined deep learning training method based on the privacy protection technology is achieved. In the invention, each participant first trains a local model on a private data set to obtain a local gradient, then performs Laplace noise disturbance on the local gradient, encrypts the local gradient and sends the encrypted local gradient to a cloud server; The cloud server performs aggregation operation on all thereceived local gradients and the ciphertext parameters of the last round, and broadcasts the generated ciphertext parameters; And finally, the participant decrypts the received ciphertext parameters and updates the local model so as to carry out subsequent training. According to the method, a homomorphic encryption scheme and a differential privacy technology are combined, a safe and efficient deep learning training method is provided, the accuracy of a training model is guaranteed, and meanwhile a server is prevented from inferring model parameters, training data privacy and internal attacksto obtain private information.

Description

Technical field [0001] The invention belongs to the field of artificial intelligence technology and relates to a joint deep learning training method based on privacy protection technology. Background technique [0002] Federated Deep Learning allows participants to jointly train deep learning models without exposing private data sets. Each participant independently trains the model on the private data set and shares training results such as gradients and parameters with other participants, thereby indirectly sharing their own training data. Compared with centralized deep learning, federated deep learning does not require the collection of users' private data, has higher efficiency and stronger security, and participants hold the trained model for local prediction. [0003] Differential Privacy is a cryptographic technology that removes individual characteristics while retaining statistical characteristics to protect user privacy. The Laplacian mechanism is often used to ac...

AI Technical Summary

Problems solved by technology

Although the use of secure multi-party computing ensures the privacy of users under large-scale users, the huge communication overhead limits the practical application

Typical public key encryption schemes (such as the Pallier homomorphic encryption scheme) present a huge computational overhead when a large amount of data is aggregated, resulting in a slow network training process

In addition, differential privacy technology protects data privacy by adding noise to training data or training gradients, in order to achieve security, resulting in a reduction in model accuracy

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