Differential Privacy Protection for Support Vector Machines for Nonlinear Classification

Algorithm 2

DPSVM-KFP.

	Input: data set , samples , attributes , privacy budget .
	Output: classification accuracy with Laplace noise added.
(1)	Normalize the data set so that the features of any sample belong to the interval ;
(2)	Project the low-dimensional data to the high-dimensional feature space, and the mapped feature vector is obtained;
(3)	Describe the classification decision function as ;
(4)	Substitute the inner product of vector with kernel function and obtain the function ;
(5)	Add Laplace noise to the kernel function . The scale parameter of noise is . is the global sensitivity of the kernel function. is used to represent the kernel function after adding noise;
(6)	Train the SVM model and compute the classification decision function;
(7)	Input test data and achieve the classification accuracy with Laplace noise added.