Glossary of Terms and Popular Articles

The National Institute of Standards and Technology (NIST) has received numerous requests to provide a summary glossary for our publications and other relevant sources, and to make the glossary available to practitioners. As a result of these requests, this glossary of common security terms has been extracted from NIST Federal Information Processing Standards (FIPS), the Special Publication (SP) 800 series, NIST Interagency Reports (NISTIRs), and from the Committee for National Security Systems Instruction 4009 (CNSSI-4009). This glossary includes most of the terms in the NIST publications.

Enhancing cloud security using data anonymization (Intel website)

Intel IT is exploring data anonymization-the process of obscuring published data to prevent the identification of key information of key information- in support of our vision of a hybrid cloud computing model and our need to protect the privacy of our employees and customers. We believe data anonymization is a viable technique for enhancing the security of cloud computing.

Cloud Security Planning (Intel Guide)

Creating a cloud computing security plan should be the first consideration when switching to a cloud computing system. Whether you’re looking at creating a private cloud or leveraging a public cloud, you need to have a security strategy. Security breaches can be the direct causes of service interruptions and can contribute to lower service levels. Also, data theft resulting from a security breach could result in a real or perceived breach of customers’ trust in your organization. Cloud computing has unique security risks.

Security for Private Clouds (Intel webinar)

Security and resource allocation are the main issues with public cloud computing. One doesnt really have any control over who is managing your firewalls, who is managing the resources that your virtual machines are sitting on.

Survey on Cloud Computing

Cloud computing provides customers the illusion of infinite computing resources which are available from anywhere, anytime, on demand. Computing at such an immense scale requires a framework that can support extremely large datasets housed on clusters of commodity hardware. Two examples of such frameworks are Google’s MapReduce and Microsoft’s Dryad. First we discuss implementation details of these frameworks and drawbacks where future work is required. Next we discuss the challenges of computing at such a large scale.