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1
Intro
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Companies Computing on Private Data
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Key Challenges
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Key Contributions
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Achieving Parallelism
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Obliviousness of Graph-parallel Algorithms
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Oblivious Gather-Key Trick
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Complexity of Our Algorithms
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Experimental Setup
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Key Evaluation Results
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Running at Scale
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Conclusion
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Across Data Centers
Description:
Explore a groundbreaking framework for parallel secure computation in this IEEE Symposium presentation. Delve into GraphSC, a system designed to simplify secure code writing for non-cryptography experts, introduce parallelism to secure implementations, and maintain data privacy through obliviousness. Learn how this innovative approach enables efficient, secure execution of graph-based algorithms, including complex data mining and machine learning tasks, on large datasets. Discover the framework's ability to process graph-based algorithms with only a small logarithmic overhead compared to non-secure parallel versions. Examine the practical applications of GraphSC in big data analysis, including a secure matrix factorization implementation capable of processing 1 million ratings in 13 hours. Gain insights into achieving parallelism, ensuring obliviousness in graph-parallel algorithms, and implementing the oblivious gather-key trick. Analyze experimental results, scalability, and potential applications across data centers. Read more

GraphSC: Parallel Secure Computation Made Easy

IEEE
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