Developed an algorithm for differentially private distributed principal component analysis (PCA). The algorithm provides a way of computing the PCA subspace in a distributed setting, while satisfying differential privacy. PCA subspaces are used in numerous machine learning algorithms as a pre-processing step. This work led to this publication.