Achieving Differential Privacy with Matrix Masking in Big Data. (arXiv:2201.04211v1 [cs.CR])

Differential privacy schemes have been widely adopted in recent years to
address issues of data privacy protection. We propose a new Gaussian scheme
combining with another data protection technique, called random orthogonal
matrix masking, to achieve $(\varepsilon, \delta)$-differential privacy (DP)
more efficiently. We prove that the additional matrix masking significantly
reduces the rate of noise variance required in the Gaussian scheme to achieve
$(\varepsilon, \delta)-$DP in big data setting. Specifically, when $\varepsilon
\to 0$, $\delta \to 0$, and the sample …

big big data data matrix privacy