Big data generation method and system for preventing privacy leakage

Abstract

The invention discloses a large data generation method and system for preventing privacy leakage, and relates to the technical fields of privacy protection and data mining. Through the method of data synthesis, data that is similar to the original data but does not contain real sensitive information is generated; the generated synthetic data can be used by data analysis algorithms; the use of the generated synthetic data by data analysis algorithms can prevent privacy leakage in the process of big data analysis . Including: preprocessing raw data; estimating probability distribution of features; generating nearest neighbor model; generating random samples; validating random samples; postprocessing. The system includes a data processing module, a random sample generation module and a random sample verification module. The present invention realizes big data generation by means of synthetic data, which can not only prevent privacy leakage in original data, but also ensure the integrity of data samples without increasing the cost of big data analysis.

Description

technical field [0001] The invention relates to the technical fields of privacy protection and data mining, in particular to a big data generation method and system for preventing privacy leakage. Background technique [0002] In recent years, with the development of big data-related technologies, big data analysis has been widely used and penetrated into various fields and industries. With the help of big data analysis technology, shopping websites can recommend products of interest to users and increase sales revenue; scenic spots can predict passenger flow peaks, and take countermeasures in advance to ensure service quality; banks can analyze each transaction record to prevent unauthorized transactions. . Although big data analysis has brought many conveniences to our life and work, it has also brought some privacy issues. [0003] In order to conduct big data analysis, a large amount of user-related data, including user personal information, preferences, browsing recor...

AI Technical Summary

Problems solved by technology

[0005] Although the above existing common methods can solve the privacy leakage problem caused by sensitive data, they also have a negative impact on big data analysis

For example, data desensitization processing deletes some information in the original data, resulting in incomplete data, which is not conducive to in-depth analysis; at the same time, desensitization processing is only for more obvious private information (such as address, phone number, etc.) Sensitive processing (such as user browsing records, purchase preferences, etc.) can also reveal some privacy habits of users

In the method of encrypting or random perturbation of data, although the sensitive information is no longer visible, the encryption and perturbation operations increase the extra overhead of algorithms related to big data analysis and reduce the efficiency of the algorithm; at the same time, the original information is modified, which will affect the data Users cause certain troubles, thus affecting the effect of data analysis

Therefore, there is still a lack of a method that can maintain the integrity of the original data to the greatest extent, without increasing the overhead of big data analysis, and at the same time prevent the leakage of private information.

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