Prof. Dr. Marc Fischlin, Technische Universität Darmstadt
Prof. Dr. Alexander May, Ruhr-Universität Bochum



Since Edward Snowden's disclosures of massive abuse of huge amounts of data by NSA and other agencies, it is evident that every individual and every company which cares about the privacy of their data has to take measures to protect the data. This is even more true for outsourced data in cloud storage and cloud computing scenarios, and when handling big data through third parties such as via Amazon's Elastic MapReduce. Standard cryptographic means such as encryption in general do not work here because by the very nature of encryption, scrambling all reasonable information, the semantics of the data are hidden and cannot be used by third parties to perform operations; the option of decrypting the data for the operations would violate the idea of protecting the data from the service provider. To reconcile the need for security with the ability to outsource computations we thus need cryptography which is compatible with the desired operations. 

In 2009 researchers from IBM announced a breakthrough result in cryptography by being able to build fully homomorphic encryption schemes which would allow such operations. However, little is known about the applicability of fully homomorphic encryption to large data sets. The goal of this research project is to provide cryptographic solutions which support operations on secured big data. To be able to focus on the cryptographic challenges here, but still provide meaningful solutions for different architectures to process big data, we choose the MapReduce framework as an abstract layer on which we base our constructions on. Hence, the overall goal of the project is to make cryptographic constructions such as fully homomorphic encryption fit the requirements of outsourced big data, and to incorporate the cryptographic solutions into the MapReduce framework.

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