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Charting a security landscape in the clouds: data protection and collaboration in cloud storage

Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for comparing secure storage approaches based on their residual vulnerabilities: attack surfaces against which an approach cannot protect. Our categorization therefore ranks approaches from the weakest (the most residual vulnerabilities) to the strongest (the fewest residual vulnerabilities). In addition to the security provided by each approach, we also consider their inherent costs and limitations. This report can therefore help an organization select a cloud data protection approach that satisfies their enterprise infrastructure, security specifications, and functionality requirements.
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Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for...

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Computing on Masked Data to improve the security of big data

Published in:
HST 2015, IEEE Int. Conf. on Technologies for Homeland Security, 14-16 April 2015.

Summary

Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such untrusted servers or databases. Advances in cryptographic techniques and database technologies provide the necessary security functionality but rely on a computational model in which the cloud is used solely for storage and retrieval. Much of big data computation and analytics make use of signal processing fundamentals for computation. As the trend of moving data storage and computation to the cloud increases, homeland security missions should understand the impact of security on key signal processing kernels such as correlation or thresholding. In this article, we propose a tool called Computing on Masked Data (CMD), which combines advances in database technologies and cryptographic tools to provide a low overhead mechanism to offload certain mathematical operations securely to the cloud. This article describes the design and development of the CMD tool.
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Summary

Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such...

READ MORE

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