Verifiable multi-party k-means federated learning method with privacy protection function

Abstract

The invention relates to a verifiable multi-party kmeans federated learning method witha privacy protection function, and belongs to the technical field of data mining. The data is horizontally distributed on multiple users, and each user encrypts and uploads the respective data to the cloud server; the cloud server randomly selects an initial centroid, and calculates the square of the Euclidean distance between the data and the initial centroid by using a secure multiplication protocol and a secure distance calculation protocol; the cloud server performs distance comparison by using a security bit decomposition protocol and a security comparison protocol, and divides the data; each user updates the clustering center by using a secret sharing protocol, encrypts the clustering center and uploads the clustering center to the cloud server; and the cloud server calculates the distance between the new clustering center and the original clustering center, if the distance is smaller than a threshold value, clustering operation is ended, and otherwise, the clustering center is updated for next iteration.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a verifiable multi-party k-means federated learning method with privacy protection. Background technique [0002] With the rapid development of Internet technology and the rapid increase of data, big data analysis and machine learning algorithms are widely used in various fields. Among them, k-means clustering is a method often used in data mining. By calculating the distance between the sample and the cluster center, each object is assigned to the cluster closest to it, so that the samples in a cluster have a high similarity. . However, in real data mining, data in multiple fields are often involved, and there are barriers that are difficult to break between data sources. In most industries, data exists in the form of isolated islands. Therefore, how to conduct data analysis while meeting data privacy, security and regulatory requirements has great development prospects, tha...

AI Technical Summary

Problems solved by technology

[0005] ① Most of the schemes are two-party k-means clustering algorithms, which do not consider the data distribution in multiple parties;

[0007] ③ Did not consider the increase or decrease of the data owner's own data;

[0008] ④ It does not consider that the data owner does not want to share the data analysis results with the hostile data owner, or even share wrong information

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