In this paper, we propose a differentially-private canonical correlation analysis algorithm. Canonical correlation analysis (CCA) is often used in clustering applications for multi-view data. CCA finds subspaces for each view such that projecting each of the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. Differential-privacy is a framework for understanding the risk of inferring the data input to the algorithm based on the output. We investigate the performance of the proposed algorithm with varying privacy parameters and database parameters on synthetic and real data. Our results show that it is possible to have meaningful privacy with very good utility even for strict privacy guarantees.