MapReduce-based distributed data anonymity processing method

A technology of distributed data and processing methods, which is applied in the field of data processing to achieve efficient processing, solve the problem of insufficient server storage and computing capabilities, and improve efficiency
CN106599726AActive Publication Date: 2017-04-26徐工汉云技术股份有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
徐工汉云技术股份有限公司
Publication Date
2017-04-26

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Abstract

The invention discloses a MapReduce-based distributed data anonymity processing method, which comprises a server side and computer terminals, wherein an original data table is stored in the server side to carry out global generalization on data and give a generalized lattice which is likely to meet k- anonymity; the server side utilizes a method of bisection to allocate a computational node to each computer terminal; each computer terminal carries out computation in parallel and returns a value to the server side according to a computation condition; if the return value does not meet k- anonymity, the server side sends a descendant node determined by the method of bisection to each computer node, otherwise the server side sends an ancestor node determined by the method of bisection to a computer; and each computer terminal recalculates according to a new node given by the server side until all nodes which meet k- anonymity are found. The method solves the trouble between explosive data growth and existing server storage and computational capabilities, and the efficiency of massive data processing is improved.
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Description

technical field

[0001] The invention relates to a distributed data anonymous processing method based on MapReduce, which belongs to the technical field of data processing. Background technique

[0002] Due to the needs of knowledge decision-making, information sharing, and scientific research, data owners need to release the data to the outside world. In order to reduce the possibility of privacy leakage during the data release process, it is necessary for the data owner to perform privacy protection related processing on the data before release.

[0003] Currently, Sweeney and Samarati et al. proposed a k-anonymity privacy protection model. The k-anonymity privacy protection model can avoid connection attacks and effectively protect private data information, but does not take effective protection measures for sensitive attribute information, and there is still a risk of private data information leakage. In the case of homogeneity attack, background knowledge attack, simil...

Claims

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