A security outsourcing machine learning method based on differential privacy

Abstract

The invention discloses a security outsourcing machine learning method based on differential privacy, which belongs to the field of network space security. The method realizes that the data provider can process the data with homomorphic encryption technology without disclosing sensitive data to a third party. After uploading to the cloud server, the cloud server stores and adds noise to the encrypted data and obtains query functions through interaction with the machine learning model provider for machine learning. This method effectively combines outsourcing computing with differential privacy, which not only ensures the security and privacy of machine learning, but also greatly reduces computing overhead and computing costs and improves computing efficiency, effectively alleviating the problems faced by traditional outsourcing machine learning methods. inefficiencies and security issues.

Description

technical field [0001] The invention belongs to the field of cyberspace security, and in particular relates to a security outsourcing machine learning method based on differential privacy. Background technique [0002] With the development of the Internet and information technology, more and more data are generated and utilized. According to statistics, the current global data growth rate is about 40% per year, and the global big data industry will develop strongly in the next five years. Faced with the ever-increasing mass of data, cloud computing technology, as a new data computing and storage model, can greatly meet its storage and processing requirements. Through the storage and computing outsourcing capabilities of cloud computing technology, users can transfer local computing and storage needs to the cloud, and use the powerful computing and storage capabilities of cloud servers to improve the efficiency of data processing. Therefore, cloud computing technology with ...

AI Technical Summary

Problems solved by technology

[0009] In order to solve the low efficiency problem caused by adding different types of noise in the data set in the traditional solution, the present invention provides a secure outsourcing machine learning method based on differential privacy, combining cloud computing technology and differential privacy technology to combine complex computing and storage tasks Outsourcing not only ensures the security and privacy of machine learning, but also greatly reduces computing overhead and cost and improves computing efficiency, effectively alleviating the inefficiency and security problems faced by traditional outsourcing machine learning methods

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