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Privacy protection weighted network release data set construction method

A weighted network and data publishing technology, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of adding data noise, etc., achieve the effect of data utility balance, reduce data noise, and improve data availability

Inactive Publication Date: 2018-03-30
SHAANXI NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the problem of too much noise in the current added data, and provide a method for constructing a privacy-preserving weighted network-published data set that achieves a balance between privacy protection and data availability

Method used

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  • Privacy protection weighted network release data set construction method
  • Privacy protection weighted network release data set construction method
  • Privacy protection weighted network release data set construction method

Examples

Experimental program
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Effect test

Embodiment 1

[0042] Taking the original data set geom.net of author cooperation weighted network in the field of computational geometry as an example, the construction method of the privacy-preserving weighted network published data set in this embodiment (see figure 1 )Proceed as follows:

[0043] (1) Determine the candidate segmentation points of the weighted network original data set

[0044] The candidate split points of the weighted network raw data set are determined by the following formula:

[0045] c i =(w max -w min )×i / m (1)

[0046] where w max for w min for v is the record weight in the weighted network original data set, D is the weighted network original data set, i is an integer from 1 to 10, and m is 10; according to formula (1), the candidate segmentation points of the weighted network original data set are 8.6000, 16.2000 ,23.8000,31.4000,39.0000,46.6000,54.2000,61.8000,69.4000,77.0000. w in formula (1) max is 77, w min is 1.

[0047] (2) Select the actual ...

Embodiment 2

[0076] Taking the original data set geom.net of author cooperation weighted network in the field of computational geometry as an example, the steps of the construction method of the privacy-preserving weighted network published data set in this embodiment are as follows:

[0077] (1) Determine the candidate segmentation points of the weighted network original data set

[0078] This step is the same as in Example 1.

[0079] (2) Select the actual segmentation point from the candidate segmentation points

[0080] Using the differential privacy index mechanism method to determine the probability o i :

[0081] o i =exp(ε 1 ×q i / (2Δq)) (2)

[0082] where ε 1 is half of the total privacy budget ε, ε is 0.5, ε 1 is 0.25, q i For weights falling into the interval [c i-1 ,c i ] number of records, c 0 is w min is 1, Δq is 1, and the candidate segmentation point corresponding to the maximum probability is selected as the actual segmentation point r, and the actual segmenta...

Embodiment 3

[0108] Taking the original data set geom.net of author cooperation weighted network in the field of computational geometry as an example, the steps of the construction method of the privacy-preserving weighted network published data set in this embodiment are as follows:

[0109] (1) Determine the candidate segmentation points of the weighted network original data set

[0110] This step is the same as in Example 1.

[0111] (2) Select the actual segmentation point from the candidate segmentation points

[0112] Using the differential privacy index mechanism method to determine the probability o i :

[0113] o i =exp(ε 1 ×q i / (2Δq)) (2)

[0114] where ε 1 is half of the total privacy budget ε, ε is 0.1, ε 1 is 0.05, q i For weights falling into the interval [c i-1 ,c i ] number of records, c 0 is w min is 1, Δq is 1, select the candidate segmentation point corresponding to the maximum probability as the actual segmentation point r, and the actual segmentation poin...

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Abstract

The invention discloses a privacy protection weighted network release data set construction method. The method comprises the steps of determining candidate segmentation points of a weighted network original data set; selecting actual segmentation points from the candidate segmentation points; constructing a big weight data set and a small weight data set; determining smoothing sensitivity of the big weight data set and the small weight data set; adding Laplace noises to a big weight data set record and a small weight data set record; and constructing a privacy protection weighted network release data set. Compared with an existing method for adding the Laplace noises to the whole data set by adopting global sensitivity, the privacy protection weighted network release data set constructionmethod has the advantages that the added data noises can be reduced while the data is ensured to meet differential privacy protection requirements; the data availability is improved; the balance of privacy protection and data utility is realized; and the privacy protection weighted network release data set construction method can be used for constructing the privacy protection weighted network release data set.

Description

technical field [0001] The invention belongs to the technical field of data privacy protection, and in particular relates to the construction of privacy protection weighted network release data sets. Background technique [0002] With the rapid development of smart devices and information technology, in order to provide better services and customer experience, operators and managers have collected a large amount of information from various users for data analysis and mining; at the same time, they have also brought sensitive information of users risk of leakage. [0003] In order to protect the privacy of users, businesses and academia have carried out a lot of research work on privacy protection, and proposed a variety of privacy protection models based on K-anonymity, l-diversification and t-proximity, mainly using generalization, suppression and many other methods of anonymous operation, but these methods rely on the background knowledge that the attacker has. As a new ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/215
Inventor 卢俊岭王小明张立臣林亚光
Owner SHAANXI NORMAL UNIV
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