Differential Privacy Principal Component Analysis for Support Vector Machines

Algorithm 2

Principal component analysis-differential privacy support vector machine (PCA-DPSVM).

	Input: data , samples n, attributes d, privacy budget ;
	Output: noised classification decision function
(1)	Compute covariance matrix of input data ;
(2)	Compute eigenvalues and corresponding eigenvectors of the covariance matrix ;
(3)	Select first k eigenvectors to determine the low-dimensional data ;
(4)	Compute classification function ;
(5)	Add noise