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Practical Conﬁdentiality Preserving Big Data Analysis
Julian James Stephen, Savvas Savvides, Russell Seidel, and Patrick Eugster, Purdue University
The “pay-as-you-go” cloud computing model has strong potential for efficiently supporting big data analysis jobs expressed via data-flow languages such as Pig Latin. Due to security concerns—in particular leakage of data — government and enterprise institutions are however reluctant to moving data and corresponding computations to public clouds. We present Crypsis, a system that allows execution of MapReduce-style data analysis jobs directly on encrypted data. Crypsis transforms data analysis scripts written in Pig Latin so that they can be executed on encrypted data. Crypsis to that end employs existing practical partially homomorphic encryption schemes, and adopts a global perspective in that it can perform partial computations on the client side when PHE alone would fail. We outline the original program transformations underlying Crypsis for reducing the cost of data analysis computations in this larger perspective. We show the practicality of our approach by evaluating Crypsis on standard benchmarks.
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