Multi-attribute data privacy removing method taking practicability into consideration

A multi-attribute, practical technology, applied in the field of information hiding, can solve the problem of not having enough practical feedback from users

Active Publication Date: 2017-11-17
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, the above-mentioned existing privacy protection

Method used

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  • Multi-attribute data privacy removing method taking practicability into consideration
  • Multi-attribute data privacy removing method taking practicability into consideration
  • Multi-attribute data privacy removing method taking practicability into consideration

Examples

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Embodiment Construction

[0044] In this embodiment, the data set used is part of the public microdata sample data (PUMS) collected during the 2015 Wyoming census in the United States. There is a lot of information on households in this dataset.

[0045] The multi-attribute data de-privacy method considering practicality in this embodiment includes the following steps:

[0046] Step 1: Import the preprocessed multi-attribute data, and extract the following four attributes from the dataset: "Insurance Expenditure" (per year), "Family Income" (in the past year), "Children" (children under the age of 18 in the family number of persons), and "elderly" (the number of persons in a household over the age of 65); where household income is considered a sensitive attribute requiring protection.

[0047] Step 2: Define necessary attributes and sensitive attributes according to the attribute description, set the pre-grouping rules for the necessary attributes, and define the order of the necessary attributes acco...

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PUM

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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.

Description

technical field [0001] The invention relates to the technical field of information hiding, in particular to a multi-attribute data privacy removal method considering practicability. Background technique [0002] Data owners need to consider whether the data involves sensitive information of individuals before displaying the data or making the data public for analysis. If related issues are involved, the data needs to be deprived of privacy in advance. [0003] The prior art Zhongguan method mainly includes the following three aspects: The first aspect is the privacy protection model. In the field of privacy protection, many automatic methods have been proposed, among which the semantic anonymity model and the differential privacy model are the two most common 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-dive...

Claims

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Application Information

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IPC IPC(8): G06F21/62
Inventor 陈为王叙萌关会华陈文龙劳天溢
Owner ZHEJIANG UNIV
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