1. A computer implemented method for data obfuscation and right-protection, the method comprising: accessing, by a computer, an initial matrix X i , the initial matrix X i representing an initial data set; and obtaining, by a computer, from the initial matrix X i , a final matrix X f , wherein obtaining the final matrix X f further comprises: projecting the initial matrix X i in a space having a lower dimension than the initial matrix X i ; obtaining a projected matrix X Proj by the following operation: X Proj =P(X i); obfuscating the projected matrix X Proj to obtain a private matrix X Priv , the private matrix X Priv obtained by the following operation: X Priv =X Proj +E=P(X i )+E, the noise matrix E is added to the projected matrix X Proj , to embed noise; and multiplying the private matrix X Priv by the matrix F, the final matrix X f obtained by the operations X f =(P(X i )+E)F is right-protected, wherein the final matrix X f is obtained by performing one of the following operations: 
 X f =( P ( X i )+ E ) F; 
 X f = P ( X i ) F+E ; and 
 X f = P ( X i F )+ E; wherein the final matrix X f further includes one or more of: a secret, the secret based on a random vector; a multiplicative noise value, having a lower magnitude than the obfuscated embedded noise; the matrix F, wherein a leading term is identity by a matrix I, the matrix I including one or more higher-order terms of the perturbation series of the matrix F, the secret, the multiplicative noise value, and a right-protected matrix, the right-protected matrix being multiplied by the matrix F; a perturbation matrix, the perturbation matrix being a matrix only using a perturbation series of the identity matrix I; a matrix F I , the matrix F I can be represented by an equation F I =I+I 1 , wherein a matrix I 1 embeds the secret and multiplicative noise; a matrix I 1w , the matrix I 1w further comprising pW, wherein W being a diagonal matrix containing a watermark w on its diagonal and p is a predetermine scalar value such that pW that has a lower magnitude than the added noise matrix E, the watermark w being a random vector with independent and identically distributed {−1,1} entries at positions indexed by a value S, the value S being an index set based on a fraction of the largest columns; a matrix Fw, the matrix Fw represented by the equation Fw=I+I 2 , wherein the matrix I 2 comprises p 1 W 1 , W 1 being a diagonal matrix containing a watermark w 1 on its diagonal, w 1 being a second random vector and p 1 is a scalar inversely proportional to theta value, the theta value being a lower magnitude than an added Gaussian noise value, the added Gaussian noise value being embedded through the noise matrix E; and a magnitude value of the secret, the magnitude value of the secret being multiplicative noise such that the preserve pairwise distances in the private matrix X Priv is preserved to a predetermined degree, wherein P(.) is a projection operator that projects an input initial matrix in a space having a lower dimension than the input matrix, E represents a noise matrix, and F represents a matrix as a perturbation series; storing ell 2 norms ∥x i ∥ 2 of columns of the private matrix X Priv =[x 1 ,. .. , x n ]; generating a set of final data corresponding to the final matrix X f available to one or more third-parties; and performing datamining on the generated set of final data based on the final matrix X f .