Wishart Mechanism for Differentially Private Principal Components Analysis

Understand

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve $(\epsilon,0)$-differential privacy.

• Our mechanism uses a Wishart distribution to generate matrix noise.
• In particular, We apply this mechanism to principal component analysis.
• Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix.