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The cloud offers unprecedented access to computation. However, ensuring the privacy of that computation remains a significant challenge. In this paper, we address the problem of distributing computation onto the cloud in a way that preserves the privacy of the computation's data even from the cloud nodes themselves. The approach, called sTile, separates the computation into small subcomputations and distributes them in a way that makes it prohibitively hard to reconstruct the data. We evaluate sTile theoretically and empirically: First, we formally prove that sTile systems preserve privacy. Second, we deploy a prototype implementation on three different networks, including the globally-distributed PlanetLab testbed, to show that sTile is robust to network delay and efficient enough to significantly outperform existing privacy-preserving approaches.