A k-anonymous privacy protection method that meets the individual needs of users

Abstract

The invention discloses a k-anonymous privacy protection method that satisfies the user's individual needs, including: storing the user-defined k value in a column attribute of a data table; decomposing the data table to obtain the tuple equivalent class; each equivalence class adopts the clustering method to perform attribute generalization according to the k value; after the equivalence class is anonymously completed, it is detected whether the global anonymous requirement of each tuple is met. If the tuple far exceeding the anonymous requirement is lower than the threshold, then The algorithm ends; if the tuples far exceeding the anonymity requirement are not lower than the threshold, then the tuples far exceeding the anonymity requirement are generalized again through their original data until the tuples far exceeding the anonymity requirement in the data table reach the threshold. The k-anonymous privacy protection method of the present invention can enable users to set the protection degree of their own data, which greatly meets the individual needs of users, and can well achieve the purpose of privacy protection and realize the privacy autonomy of users.

Description

Technical field [0001] The present invention relates to the technical field of privacy protection, and specific relates to a K anonymous privacy protection method that meets user personalized demand. Background technique [0002] With the rapid development of Internet technology, large-capacity storage technology and the gradual expansion of data sharing, personal data is "explosive" in terms of species and quantity. Kevin Kelly is proposed in CEIBS Business Review. The world's fastest growing is not substance, but information, it is better than our biotechnology, human productivity. The amount of data created by human per second can only be described by 16 multiplying 276 times, and the growth rate has far exceeded the speed. Modern people pay more attention to the privacy protection of personal data. Most people are reluctant to initiate their own data. These related data are called microeses, such as demographic data, personal consumption data, medical data, etc. It is believe...

AI Technical Summary

Problems solved by technology

Afterwards, the research on k-anonymity tended to be personalized k-anonymity research, using the method of generalizing sensitive attributes or reducing the frequency of sensitive attributes below α, realizing personalized anonymity for sensitive values, and improving information publishing and sharing. The quality of the data in the data increases the usability of the data; however, the current data anonymization algorithms are all researched on the sensitive attributes of the users, and do not change the quasi-identifier attributes of the users; Privacy autonomy on quasi-identifiers cannot meet the individual needs of users

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