Research Article

Differential Privacy Location Protection Scheme Based on Hilbert Curve

Algorithm 2

Anonymous data processing procedure.
Input: -privacy budget L-location data set D-non-location data set, -non-location data attribute collection, -continuous attribute data set, -discrete attribute data set, h-the height of the tree.
Output: anonymous data set T satisfying differential privacy protection.
(1)Begin Procedure
(2);//privacy budget allocation
(3)If the data belongs to location data L
(4)For any element in set L
(5); //Q is the user’s query function, which has global sensitivity and adds Laplace noise to the location data
(6)End For
(7)Else If the data belongs to nonlocation data D
(8)For any element P in set D, and the element satisfies //Attribute partition of nonlocation data
(9)If is a continuous-valued attribute, then
(10); //Adding Laplace noise to nonlocation data with the continuous-valued attribute
(11)Else If is a discrete-valued attribute, then
(12); //Laplace noise is added to nonlocation data with the discrete-valued attribute
(13)End If
(14)End For
(15)End If
(16)Return //Output anonymous data set
(17)End Procedure