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Nabil A. Schear Clear filter

A survey of cryptographic approaches to securing big-data analytics in the cloud

September 9, 2014

Conference Paper

Author:

Sophia Yakoubov

…

Published in:

HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.

Topic:

supercomputing

R&D area:

Cyber Security and Information Sciences

Summary

The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals. We then survey three cryptographic techniques - homomorphic encryption, verifiable computation, and multi-party computation - that can be used to achieve these goals. We describe the cryptographic techniques in the context of our cloud model and highlight the differences in performance cost associated with each.

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Summary

A survey of cryptographic approaches to securing big-data analytics in the cloud

Computing on masked data: a high performance method for improving big data veracity

September 9, 2014

Conference Paper

Author:

Jeremy Kepner

…

Published in:

HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.

Topic:

supercomputing

R&D area:

Cyber Security and Information Sciences

R&D group:

Secure Resilient Systems and Technology

Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.

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