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
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[0044] In this embodiment, the data set used is part of the public microdata sample data (PUMS) collected during the 2015 Wyoming State Census. This data set contains a lot of information about households.
[0045] The method for depriving privacy of multi-attribute data 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 data set: "Insurance Expenses" (annual), "Family Income" (in the past year), "Children" (in the family under 18) The number of persons), and the "elderly" (the number of persons over 65 in the family); where family income is considered a sensitive attribute that needs to be protected.
[0047] Step 2: Define the necessary attributes and sensitive attributes according to the attribute description, set the pre-grouping rules of the necessary attributes, define the order of the necessary attributes according to the attribute characteri...
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