Privacy protection k-means clustering method

Abstract

The invention relates to a negative-database-based privacy protection k-means clustering method. The method comprises the steps that each data of X in a database needing to be clustered is converted into a binary string, each binary string generates a corresponding negative database through a K-hidden algorithm; k different binary strings are generated randomly to serve as an initial clustering center; for each negative database (shown in the description), the Euclidean distance from the negative database to each clustering center is calculated, and the negative database is divided to the clustering center with the minimal Euclidean distance; for each cluster, the clustering center is recalculated; iteration is repeated till the clustering center does not change any more. Accordingly, theexisting negative-database-based k-means algorithm clustering precision can be improved, and the provided Euclidean distance estimation method can be used for other data mining algorithms to protect data privacy.

Description

Technical field [0001] The invention belongs to the field of privacy protection and security, and specifically relates to a privacy-protected k-means clustering method formed by using a negative database to protect the data privacy of the k-means algorithm. Background technique [0002] With the rapid development of computer technology, a large amount of data is produced, which also makes data mining technology develop rapidly. Many data mining algorithms have been proposed, such as k-means algorithm, k-nearest neighbor algorithm and so on. These data mining algorithms do not protect data security during operation, which may cause the user's private information to be leaked. As people pay more and more attention to privacy information, data mining for privacy protection has become particularly important. [0003] Negative database (NDB) is inspired by the artificial immune system. Unlike traditional databases, negative databases store information that is not in traditional databa...

AI Technical Summary

Problems solved by technology

NDB also supports distance metrics, but there is little work applying NDB to privacy-preserving data mining

Existing work has proved that negative databases can be used for clustering and classification to protect the privacy of the original data, but the existing privacy-preserving clustering algorithms are based on the Hamming distance, which is not applicable to most databases, and now Many clustering algorithms are based on Euclidean distance, therefore, it is necessary to propose a privacy-preserving clustering algorithm based on Euclidean distance on the negative database

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