Personalized differential privacy protection method for high-dimensional data publishing in distributed environment

Abstract

The invention discloses an individual differential privacy protection method for high-dimensional data publishing in a distributed environment. According to the method, the correlation among properties is quantified through mutual information, and the mutual information of corresponding property pairs is calculated by use of a mutual information formula; an approximate k-degree Bayesian network isconstructed according to the mutual information, and the Bayesian network can well reflect dependency among the properties; privacy budgets are allocated individually according to the quantity of sensitive properties and the quantity of non-sensitive properties meeting conditions; all participants perform noise addition processing on data according to the allocated privacy budgets, and a random response mechanism is adopted to perform noise addition; and the data obtained after noise addition is sent to a manager, the manager gathers the data and synthesizes the data into an integrated dataset, and then the dataset is published to the outside. Through the method, when the data is published, a privacy requirement is guaranteed, a large amount of processing data is reduced, therefore, change of the data is lowered, the utility of the data is improved, and the method is beneficial for a data analyzer to perform relevant analysis.

Description

technical field [0001] The invention relates to the technical field of network data security, in particular to a personalized differential privacy protection method for publishing high-dimensional data in a distributed environment. Background technique [0002] In recent years, with the rapid development of the Internet and information technology, the release of privacy-protected data has received a lot of attention, and people have a deeper understanding of the concept of protecting personal privacy and realizing data sharing. Many specific data owners need to publish their original data (such as medical data of a hospital, user data of a social networking site, etc.) for other institutions to conduct research and analysis or for other purposes. The original data to be released may contain a large amount of sensitive personal privacy information (such as salary, illness, personal savings, etc.), and direct release may lead to the disclosure of private information, so the da...

AI Technical Summary

Problems solved by technology

However, the existing differential privacy protection technology cannot deal with the release of high-dimensional data in a distributed environment very well, especially when the published data contains a large number of attribute fields, the existing technology injects a large amount of noise into the data information, which may cause the published data to lose its due effect

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