A method of processing large data based on the mldm algorithm satisfying the quadratic aggregation

A technology of big data and big data sets, which is applied in the field of privacy protection of databases and can solve problems such as high algorithm complexity and long calculation time

CN107305614BActive Publication Date: 2020-05-26XIDIAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2020-05-26

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Abstract

The invention belongs to the technical field of database privacy protection, and discloses a method for processing big data based on an MLDM algorithm meeting secondary aggregation. The method comprises the steps of introducing a k-means algorithm based on a (1,d,e)-MDAV algorithm; and when a big data set is processed, dividing the big data set into a plurality of small data sets firstly, then processing each small data set by using the (1,d,e)-MDAV algorithm, and finally combining the processed data, thereby enabling the whole data set to meet a (1,d,e)-diversity rule, and obtaining relatively low algorithm time complexity and relatively short algorithm time through improvement. According to the method, a k-means algorithm is introduced based on the (1,d,e)-MDAV algorithm; a new MLDM algorithm is proposed; and when the big data set is processed, the big data set is divided into the small data sets firstly, then each small data set is processed by using the (1,d,e)-MDAV algorithm, and finally the processed data are combined, so that the whole data set can meet the (1,d,e)-diversity rule, and the relatively low algorithm time complexity and the relatively short algorithm time can be obtained through the improvement.
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Description

technical field

[0001] The invention belongs to the technical field of privacy protection of databases, and in particular relates to a method for processing large data based on an MLDM algorithm satisfying secondary aggregation. Background technique

[0002] The issue of privacy protection in data publishing was first raised in the field of statistical leakage control, and then gradually penetrated into the entire information technology field. In the field of statistical leak control, methods such as micro-aggregation, randomization, sampling, and adding white noise are mainly used to protect information. While trying to ensure the statistics and availability of processed data and the security of private information, more information is retained. Useful information, balance the relationship between data confidentiality and availability. After years of development, the privacy protection technology released in the data mainly focuses on two aspects of research, one is the us...

Examples

Embodiment Construction

[0033] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0034] The present invention further improves on the basis of (l, d, e)-MDAV algorithm, introduces k-means algorithm, proposes MLDM algorithm, and seeks the optimization of algorithm efficiency when processing large data sets. . On the basis of the (l,d,e)-MDAV algorithm, the k-means algorithm is introduced, and a new MLDM algorithm is proposed. When the new algorithm processes a large data set, it first divides the large data set into several small data sets, and then Use the (l,d,e)-MDAV algorithm to process each small data set, and finally combine the processed data so that the entire data set satisfies the (l,d,e)-dive...