Inference attack resistant distributed multi-task learning privacy protection method and system

Abstract

According to an inference attack resistant distributed multi-task learning privacy protection method and system, model training is carried out through various task nodes based on local data, and jointmodel training is realized through a knowledge sharing mode. According to the method, a privacy protection model training mechanism based on homomorphic cryptography is provided, so that task nodes realize multi-task learning model training on the premise of ensuring the privacy of training data, the model training efficiency is independent of a sample data volume, and the machine learning modeltraining efficiency is improved. A model publishing method based on differential privacy is designed, and an identity inference attack initiated can be resisted when a model user accesses a machine learning model . The system comprises a key generation center, a central server, a task node and a model user. According to the method, the data privacy of the task nodes in the model training process and after the release of the model can be ensured, and large-scale application of multi-task machine learning is promoted.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a distributed multi-task learning privacy protection method and system against reasoning attacks, which can be used for collaborative training of multi-task models of large-scale data with different distributions. Background technique [0002] With the development of cloud computing and big data technology, machine learning technology has been applied on a large scale, especially in the fields of image recognition and intelligent speech recognition. The recognition accuracy of machine learning models has exceeded that of the human brain. Machine learning is often based on massive data for model training. However, the training data may come from different data sources, resulting in different distributions of the collected data. Therefore, traditional machine learning model training methods are difficult to be directly applied to multi-data distribution model trainin...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a privacy protection method and system for distributed multi-task learning against inference attacks to ensure privacy protection during the training process of the model in the prior art. Data privacy of task nodes after model release, promoting large-scale application of multi-task machine learning

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