A multi-attribute data deprivation method considering practicability

A multi-attribute, practical technology, applied in the field of information hiding, can solve the problem of not having enough practical feedback from users
CN107358115BActive Publication Date: 2019-09-20ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Publication Date
2019-09-20

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Abstract

The invention discloses a multi-attribute data privacy removing method taking practicability into consideration. The method comprises the following steps of S1, importing preprocessed multi-attribute data; S2, defining necessary attributes and sensitive attributes according to attribute descriptions, setting pre-grouping rules of the necessary attributes, and defining the sequences of the necessary attributes according to the attribute characteristics; S3, establishing a privacy exposure risk tree, and taking the attribute sequences and the pre-grouping rules in the step S2 as hierarchical sequences of the privacy exposure risk tree and the basis for generating branches of each hierarchy; and S4, measuring result information according to coding risks of the sensitive attributes on nodes and borders of the privacy exposure risk tree. According to the method, the suitable method can be flexibly selected from a plurality of common grammar anonymization and difference privacy models for solving the privacy problem, thereby satisfying various privacy demands for different data. Through utilization of special advantages of the privacy exposure risk tree, a multi-dimensionally aggregated space is designed compactly.
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Description

Technical field

[0001] The invention relates to the technical field of information hiding, in particular to a method for depriving privacy of multi-attribute data considering practicability. Background technique

[0002] Before displaying the data or making the data public for analysis, the data owner needs to consider whether the data involves sensitive information of the individual. If related issues are involved, the data needs to be deprived of privacy in advance.

[0003] The prior art Zhongguan methods mainly include the following three aspects: First, privacy protection models. In the field of privacy protection, many automatic methods have been proposed. Among them, semantic anonymity models and differential privacy models are the two most common types of privacy protection models. Among them k-anonymity (L. Sweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05): 557-570, 2002.), l-diversity...

Claims

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