Privacy data hierarchical protection method based on big data

Abstract

The invention discloses a privacy data grading protection method based on big data, which comprises the following steps: 1) obtaining keywords, and setting a privacy degree grade; dividing the acquired keywords into different grades according to privacy degrees, and establishing a storage directory for each keyword, wherein a plurality of keywords exist under each privacy degree grade; 2) selecting one keyword in one privacy degree level to retrieve the database, and classifying the data searched by using the keyword into a storage directory of the keyword; and 3) repeating the operation in the step 2) until all keywords in the privacy degree level selected in the step 2) are traversed. According to the technical scheme, the high-frequency words in the private data serve as the keywords, the sensitivity values of the keywords are calculated, and the sensitivity values serve as the grading standard of the private data, so that grading of the private data has a quantitative standard, grading of the private data is accurate, and the private data is protected conveniently.

Description

technical field [0001] The invention relates to a hierarchical protection method for private data based on big data, which belongs to the field of databases. Background technique [0002] The vigorous development of information technologies such as the Internet, cloud computing, and big data has given birth to many open and interconnected information networks (Information Networks). In these information networks, user data is often scattered and stored in multiple service providers (SPs for short) in the network. For example, in medical information networks, patient medical record information is often stored in multiple hospitals. Through distributed query processing, the aggregation, association and analysis of data scattered in various SPs is not only the direct demand of users themselves, but also the inevitable demand for mining data value, facilitating public management, and promoting social development. However, these data often contain a large amount of private infor...

AI Technical Summary

Problems solved by technology

[0003] Big data has the characteristics of massive data scale, diverse data types, and low value density. Therefore, it is difficult to use manual grading and manual review for private data in big data, and it is also difficult for users to be sensitive to private data. Query and statistics are carried out manually, which makes it difficult to adjust the classification of private data in big data

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