Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8249-8279, 2022.
Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streaming data sampled from a distribution and arrives sequentially. We also consider the continual release model where parameters related to private information are updated and released upon each new data. Numerous algorithms have been developed to achieve optimal excess risks in different $\ell_p$ norm geometries, but none of the existing ones can be adapted to the streaming and continual release setting. We propose a private variant of the Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data. Combined with the adaptive DP analysis, our algorithm achieves the first optimal excess risk in linear time in the case $1