Differential Privacy Protection for Support Vector Machines for Nonlinear Classification

Algorithm 1

DPSVM-TDP.

	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)	Add Laplace noise to the training data set . The scale parameter of noise is . is the global sensitivity of the training data set. is used to represent the training data set after adding noise;
(3)	Project the low-dimensional data to the high-dimensional feature space, and the mapped feature vector is obtained;
(4)	Describe the classification decision function as ;
(5)	Substitute the inner product of vector with kernel function and obtain the function ;
(6)	Train the SVM model and compute the classification decision function;
(7)	Input test data and achieve the classification accuracy with Laplace noise added.