Practical Confidentiality Preserving Big Data Analysis in Untrusted Clouds
Savvas Savvides - Purdue University
Jan 28, 2015Size: 148.3MB
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AbstractThe “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. In this talk we will discuss 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 (PHE) schemes, and adopts a global perspective in that it can perform partial computations on the client side when PHE alone would fail.
About the SpeakerSavvas Savvides is a PhD student in Computer Science at Purdue University. He earned his Master’s degree in Computer Science from New York University and his Bachelor’s in Computer Science from the University of Manchester. His primary research interests include Information Security, Distributed Systems and Cloud Computing. His current research focus is on devising practical solutions for confidentiality preserving big data analysis jobs.
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