Utpala, S., Vepakomma, P., Miolane, N.
Abstract
Differential privacy has become crucial in the real-world deployment of statistical and machine learning algorithms with rigorous privacy guarantees. The earliest statistical queries, for which differential privacy mechanisms have been developed, were for the release of the sample mean. In Geometric Statistics, the sample Fréchet mean represents one of the most fundamental statistical summaries, as it generalizes the sample mean for data belonging to nonlinear manifolds. In that spirit, the only geometric statistical query for which a differential privacy mechanism has been developed, so far, is for the release of the sample Fréchet mean: the \emph{Riemannian Laplace mechanism} was recently proposed to privatize the Fréchet mean on complete Riemannian manifolds. In many fields, the manifold of Symmetric Positive Definite (SPD) matrices is used to model data spaces, including in medical imaging where privacy requirements are key. We propose a novel, simple and fast mechanism - the \emph{tangent Gaussian mechanism} - to compute a differentially private Fréchet mean on the SPD manifold endowed with the log-Euclidean Riemannian metric. We show that our new mechanism has significantly better utility and is computationally efficient -- as confirmed by extensive experiments.
Citation
Utpala, S., Vepakomma, P., Miolane, N. Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices. Transactions of Machine Learning Research {
BibTeX
@article{utpala2022differentially, title={Differentially Private Fr$\backslash$'echet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric}, author={Utpala, Saiteja and Vepakomma, Praneeth and Miolane, Nina}, journal={Transactions of Machine Learning Research}, year={2022} }
