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Obfuscation and protection of data rights

  • US 9,916,472 B2
  • Filed: 07/22/2015
  • Issued: 03/13/2018
  • Est. Priority Date: 07/22/2015
  • Status: Expired due to Fees
First Claim
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1. A computer implemented method for data obfuscation and right-protection, the method comprising:

  • accessing, by a computer, an initial matrix Xi, the initial matrix Xirepresenting an initial data set; and

    obtaining, by a computer, from the initial matrix Xi, a final matrix Xf, wherein obtaining the final matrix Xf further comprises;

    projecting the initial matrix Xi in a space having a lower dimension than the initial matrix Xi;

    obtaining a projected matrix XProj by the following operation;

    XProj=P(Xi);

    obfuscating the projected matrix XProj to obtain a private matrix XPriv, the private matrix XPriv obtained by the following operation;

    XPriv=XProj+E=P(Xi)+E, the noise matrix E is added to the projected matrix XProj, to embed noise; and

    multiplying the private matrix XPriv by the matrix F, the final matrix Xf obtained by the operations Xf=(P(Xi)+E)F is right-protected,wherein the final matrix Xf is obtained by performing one of the following operations;


    Xf=(P(Xi)+E)F;


    Xf=P(Xi)F+E; and


    Xf=P(XiF)+E;

    wherein the final matrix Xf 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 FI, the matrix FI can be represented by an equation FI=I+I1, wherein a matrix I1 embeds the secret and multiplicative noise;

    a matrix I1w, the matrix I1w 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+I2, wherein the matrix I2 comprises p1W1, W1 being a diagonal matrix containing a watermark w1 on its diagonal, w1 being a second random vector and p1 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 XPriv 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 ell2 norms ∥

    xi

    2 of columns of the private matrix XPriv=[x1, . . . , xn];

    generating a set of final data corresponding to the final matrix Xf available to one or more third-parties; and

    performing datamining on the generated set of final data based on the final matrix Xf.

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