Gradient descent calculation method for protecting privacy data

Abstract

The invention discloses a gradient descent calculation method for protecting privacy data. The method is used in gradient function computation of machine learning including one or more data providers,a decryption service provider, and a computing resource provider,. The method is suitable for carrying out fitting calculation on a sigmoid function by utilizing a polynomial function similar to thesigmoid function contour or carrying out fitting calculation on the sigmoid function by utilizing a piecewise function similar to the sigmoid function contour. The method comprises the steps of homomorphic encryption key generation and distribution, training parameter negotiation, data encryption and summarization and gradient descent. The method is high in precision, and precision loss caused bydata processing in the calculation process is within a controllable range; the security is high, and the input data and the intermediate data can meet the semantic security requirements in the calculation process; flexibility is good, and two or more participants can participate in calculation; and the expansibility is good, and the original gradient descent can be expanded to a Newton method or abatch gradient descent.

Description

technical field [0001] The invention relates to the field of privacy protection of machine learning, in particular to a gradient descent calculation method for protecting privacy data in machine learning. Background technique [0002] In modern society, machine learning technology is more and more widely used in various fields, such as medical care, business, education and public safety. However, a large amount of private data is involved in the process of machine learning, especially in the scenario where these data belong to different data providers, there are a series of threats of privacy leakage. Therefore, machine learning algorithms with privacy protection have always been academic research. A research hotspot in the world. Among them, the gradient descent algorithm is an important optimization method in the field of machine learning, which is widely used in the training process of various machine learning algorithms, including algorithms such as logistic regression,...

AI Technical Summary

Problems solved by technology

[0003] For the protection of private data in the gradient descent algorithm, most of the existing schemes adopt some less secure linear encryption or differential privacy methods to design schemes with certain privacy protection, but these schemes are difficult to ensure that all private data will not be Give way

There are also some schemes that use fully homomorphic encryption schemes to encrypt original data while ensuring data confidentiality and computing power, but the efficiency of fully homomorphic encryption algorithms at this stage is low, which limits the practical application of these schemes

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