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Numerical linear algebra plays an important role in computer science. In this paper, we initiate the study of performing linear algebraic tasks while preserving privacy when the data is streamed online. Our main focus is the space requirement of the privacy-preserving data-structures. We give the first sketch-based algorithm for differential privacy. We give optimal, up to logarithmic factor, space data-structures that can compute low rank approximation, linear regression, and matrixarXiv:1409.5414v5 fatcat:dksjsngdefgyjhs5rhiewkhdg4