A sensitive information desensitization method and system for data sharing

Abstract

The invention relates to a sensitive information desensitization method and system for data sharing. According to the method, by adopting statistics, a natural language processing technology and a machine learning technology, the protection of sensitive data in the whole process from data publishing to data application and usage, and automatic identification of sensitive information of such as named entities and addresses is put forward on the basis of the establishment of a sensitive information keyword library, named entities, addresses and the like is put forward; a Sigmoid function is used for calculating the correlation degree of sensitive attributes; a desensitization strategy is carried out in a combined mode of establishing a sensitive attribute generation rule library and adopting a named entity desensitization rule and a core desensitization algorithm; deep desensitization calculation is conducted in combination with numerical sensitive attributes and classified sensitive attributes separately, the desensitization degree of a whole data set is obtained, and the controlled output of data and the like are achieved by downloading a link address hash; thus, the safety of data sensitive information and a sensitive information processing strategy which meets analysis and mining requirements to the maximize degree can be ensured, and the method and system have the advantages of being good in desensitization effect, high in reliability and the like.

Description

technical field [0001] The present invention relates to the intersecting technical field of information technology and data security, in particular to a sensitive information desensitization method and system for data sharing. Background technique [0002] In recent years, the integration of information technology and economic society has triggered the rapid growth of data, and data has become an important development resource. In 2016, the government vigorously promoted the interconnection, openness and sharing of information systems and public data, accelerated the integration of government information platforms, eliminated information islands, recommended data resources to be opened to the society, guided social development, and better served the public. However, in the context of big data, open data sharing also brings challenging issues. Various data leakage incidents occur frequently, such as the information leakage incident of nearly 6,000 newborns in Anhui, the annua...

AI Technical Summary

Problems solved by technology

Patent No. CN201510755773.5 discloses a format-preserving desensitization method for different types of private data, so that it can be stored in ciphertext, which can avoid the ciphertext length being greater than the defined length of the table field, resulting in data loading, and avoiding the number The type of the encrypted field does not match the source data type, resulting in an error in data loading

[0004] However, in the above-mentioned desensitization system or desensitization method, there are certain limitations.

The main reasons are: (1) most desensitization systems and methods are aimed at the structured data of the database, but how to deal with unstructured data (such as text data) is not involved; (2) lack of consideration of sensitive data desensitization If the desensitization depth of sensitive data is not enough, prevent the use of non-sensitive data to reconstruct sensitive data; (3) the uniqueness of identification and format consistency requirements after data desensitization cannot be guaranteed. For example, hospital data is generally identified by ID number And to locate individuals, if the desensitization algorithm or encryption algorithm is used, the identity card information will lose the uniqueness of the identity and the consistency of the format

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