[Retracted] Differentially Private Singular Value Decomposition for Training Support Vector Machines

Table 1

Symbols.

Symbol	Description
D, D′	The adjacent matrix of datasets
A,	Matrix of covariance
.	Train instance
.	Label
Α	Dual vector
Q	Symmetric matrix for kernel function
K	Kernel function
E	Vector composed entirely of ones
C	Upper limit of α
λi	Eigenvalue
.	Eigenvector
Γ	The accumulative contribution rate of principal components
U, V	The singular vectors or eigenvectors matrix
∑, S	The singular values or eigenvalues diagonal matrix
σi	Singular value
I	Unit diagonal matrix
M	A randomized mechanism
O	All subsets of possible outcomes of mechanism M
ɛ	Privacy budget
β, δ	Privacy parameter
S1, S2	The and sensitivity of function
Laplace(b)	Laplace noise (mean: 0; scale: b)
N(0, τ^2)	Gaussian noise with (mean: 0; deviation: τ)