Safe data-outsourcing machine learning and data analysis method

Abstract

The invention discloses a safe data-outsourcing machine learning and data analysis method. A random tree is transformed in a homoenergetic mode into properties of a binary tree; a trained decision tree prediction model is transformed into the binary tree, the binary tree is induced into a special polynomial of which a shape is a sum of an infinite number of multiplier items, and each piece of data in the model is encrypted by an RSA and is uploaded to a cloud platform; then data which needs to be decided is also encrypted by the RSA and is uploaded to the cloud platform; the encrypted data of the model and the encrypted data which needs to be decided are correspondingly computed by utilizing the multiplication homomorphic encryption property of the RSA to obtain a ciphertext result of each multiplier item; and the results are returned to a data user for decryption so as to obtain a decision result. By transforming the binary tree into the polynomial, a decision tree which originally can be implemented by various computations is transformed into the decision tree which can be implemented by one computation, so that the machine learning process of the decision tree can be rapidly completed by using a multiplication homomorphic algorithm, a complex degree of carrying out such decision tree machine learning algorithm on a ciphertext is greatly reduced, and computing efficiency is promoted.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to a safe data analysis method for data outsourcing machine learning. Background technique [0002] With the development of science and technology, the advantages of cloud platforms (Cloud Platforms), such as huge scale, fast virtualization, strong versatility, on-demand service and very low cost, have become increasingly prominent, which makes high-speed computing and efficient storage of big data a reality. However, the cloud platform has always had huge security risks, and data leakage incidents are extremely prone to occur. In just a few years, shocking information leakage incidents have occurred frequently: Anthem, the second largest medical insurance company in the United States, lost 80 million personal information, and the resume data of 58, a well-known domestic job search platform, was sold at a low price. Personal privacy data is stored centrally...

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the prior art above, the purpose of the present invention is to provide a safe data analysis method for outsourcing machine learning, which solves the contradiction between the security and processability of data on the existing cloud platform, that is, the data is uploaded after encryption The cloud platform cannot process it, but after decryption, the processing will expose the data to the risk of leakage

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