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WATCH RELATED VIDEO: ОБЗОР ПРОЕКТА VERASITY И КРИПТОВАЛЮТЫ VRA!

Introduction


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Create your free account to continue reading. Sign Up. Upcoming SlideShare. Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Share Email. Top clipped slide. David Sweigert Follow. Defensive cyber security expert. Law Enforcement Cyber Incident Reporting. National Cyber Security Awareness Month poster. Congressional support of Ethical Hacking and Cyber Security. Related Books Free with a 30 day trial from Scribd. Elsevier Books Reference.

Germany, September Elsevier Books Reference. Related Audiobooks Free with a 30 day trial from Scribd. Willie E. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.

There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by Federal agencies even before the completion of such companion publications.

Thus, until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, Federal agencies may wish to closely follow the development of these new publications by NIST.

Organizations are encouraged to review all draft publications during public comment periods and provide feedback to NIST. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology IT.

Abstract Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics.

This volume, Volume 7, contains summaries of the work presented in the other six volumes and an investigation of standards related to Big Data. As of the date of this publication, there are over six hundred NBD-PWG participants from industry, academia, and government. Shawn Miller U. Three versions are planned for this volume, with Versions 2 and 3 building on the first. Further explanation of the three planned versions and the information contained therein is included in Section 1.

Please be as specific as possible in any comments or edits to the text. Specific edits include, but are not limited to, changes in the current text, additional text further explaining a topic or explaining a new topic, additional references, or comments about the text, topics, or document organization. These specific edits can be recorded using one of the two following methods.

Please contact Wo Chang wchang nist. The current effort documented in this volume reflects concepts developed within the rapidly27 evolving field of Big Data. The growth42 rates for data volumes, speeds, and complexity are outpacing scientific and technological advances in data43 analytics, management, transport, and data user spheres.

The initiative also challenged61 industry, research universities, and nonprofits to join with the federal government to make the most of the62 opportunities created by Big Data. Forum participants noted that this68 roadmap should define and prioritize Big Data requirements, including interoperability, portability,69 reusability, extensibility, data usage, analytics, and technology infrastructure. In doing so, the roadmap70 would accelerate the adoption of the most secure and effective Big Data techniques and technology.

Such a76 consensus would create a vendor-neutral, technology- and infrastructure-independent framework that77 would enable Big Data stakeholders to identify and use the best analytics tools for their processing and78 visualization requirements on the most suitable computing platform and cluster, while also allowing79 value-added from Big Data service providers. Potential areas of future work for the Subgroup during stage 2 are highlighted in Section 1. The current effort documented in this volume reflects concepts developed within the rapidly evolving field of Big Data.

The aim is to create vendor-neutral, technology- and infrastructure agnostic deliverables to enable Big Data stakeholders to select the best analytic tools for their processing and visualization requirements on the most suitable computing platforms and clusters while allowing value-added from Big Data service providers and flow of data between the stakeholders in a cohesive and secure manner.

This included setting standardization and adoption priorities through an understanding of what standards are available or under development as part of the recommendations. In the first phase, the Subgroup focused on the identification of existing standards relating to Big Data and inspection of gaps in those standards.

These two concepts, Big Data and data science, are broken down into individual terms and concepts in the following subsections. As a basis for discussions of the NBDRA and related standards and measurement technology, associated terminology is defined in subsequent subsections.

Each of these characteristics influences the overall design of a Big Data system, resulting in different data system architectures or different data lifecycle process orderings to achieve needed efficiencies.

A number of other terms are also used, several of which refer to the analytics process instead of new Big Data characteristics. The fourth paradigm is a term coined by Dr. Jim Gray in to refer to the conduct of data analysis as an empirical science, learning directly from data itself. Data science as a paradigm would refer to the formulation of a hypothesis, the collection of the data—new or pre existing—to address the hypothesis, and the analytical confirmation or denial of the hypothesis or the determination that additional information or study is needed.

As in any experimental science, the end result could in fact be that the original hypothesis itself needs to be reformulated. The key concept is that data science is an empirical science, performing the scientific process directly on the data.

Note that the hypothesis may be driven by a business need, or can be the restatement of a business need in terms of a technical hypothesis. Data science can be understood as the activities happening in the data layer of the system architecture to extract knowledge from the raw data. To this end, the NBD-PWG collected use cases to gain an understanding of current applications of Big Data, conducted a survey of reference architectures to understand commonalities within Big Data architectures in use, developed a taxonomy to understand and organize the information collected, and reviewed existing Big Data relevant technologies and trends.

The development of requirements included gathering and understanding various use cases from the nine diversified areas, or application domains, listed below.

Requirements are the challenges limiting further use of Big Data. The Subgroup surveyed currently published Big Data platforms by leading companies or individuals supporting the Big Data framework and analyzed the collected material. This effort revealed a remarkable consistency between Big Data architectures. The dark blue boxes contain the name of the role at the top with potential actors listed directly below. It does not represent the system architecture of a specific Big Data system, but rather is a tool for describing, discussing, and developing system-specific architectures using a common framework of reference.

The reference architecture achieves this by providing a generic high-level conceptual model that is an effective tool for discussing the requirements, structures, and operations inherent to Big Data. The model is not tied to any specific vendor products, services, or reference implementation, nor does it define prescriptive solutions that inhibit innovation.

The requirements from each category were used as input for the development of the corresponding NBDRA component. This conceptual model, the NBDRA, is shown in Figure 2 and represents a Big Data system comprised of five logical functional components connected by interoperability interfaces Two fabrics envelop the components, representing the interwoven nature of management and security and privacy with all five of the components.

It provides a framework to support a variety of business environments, including tightly-integrated enterprise systems and loosely-coupled vertical industries, by enhancing understanding of how Big Data complements and differs from existing analytics, business intelligence, databases, and systems.

Along the information axis, the value is created by data collection, integration, analysis, and applying the results following the value chain. Along the IT axis, the value is created by providing networking, infrastructure, platforms, application tools, and other IT services for hosting of and operating the Big Data in support of required data applications. At the intersection of both axes is the Big Data Application Provider component, indicating that data analytics and its implementation provide the value to Big Data stakeholders in both value chains.

Data flows between the components either physically i. While the main focus of the NBDRA is to represent the run-time environment, all three types of communications or transactions can happen in the configuration phase as well.

Manual agreements e. In system development, actors and roles have the same relationship as in the movies, but system development actors can represent individuals, organizations, software, or hardware.

According to the Big Data taxonomy, a single actor can play multiple roles, and multiple actors can play the same role. The NBDRA does not specify the business boundaries between the participating actors or stakeholders, so the roles can either reside within the same business entity or can be implemented by different business entities. Therefore, the NBDRA is applicable to a variety of business environments, from tightly-integrated enterprise systems to loosely-coupled vertical industries that rely on the cooperation of independent stakeholders.

As a result, the notion of internal versus external functional components or roles does not apply to the NBDRA. For example, a Data Consumer of one system could serve as a Data Provider to the next system down the stack or chain. Big Data is increasingly stored on public cloud infrastructure built by various hardware, operating systems, and analytical software.

Traditional security approaches usually addressed small scale systems holding static data on firewalled and semi-isolated networks. The surge in streaming cloud technology necessitates extremely rapid responses to security issues and threats.



US10237070B2 - System and method for sharing keys across authenticators - Google Patents

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in the case of other cloud-based services, IDaaS brings with it great concerns regarding security to employ proxy re-encryption techniques for achieving.

What AI Could Mean for the Creative Process…. and What It Will Mean

Being one of the most popular Linux distros, Ubuntu has an uncountable number of variants and derivatives. With the release of the latest long-term Ubuntu Along the same lines, here comes Voyager Live — the Xubuntu-based Linux distribution which has released a new version Voyager The latest long-term Voyager ships with the updated Xfce 4. The new Voyager It aims to deliver a simple, lightweight, and feature-rich operating system with Ubuntu-like experience. Voyager is an international version that features all basic languages and translations.


FreeBSD Manual Pages

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Climate Science Glossary

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Cyber-Security Issues in Healthcare Information Technology

Use the controls in the far right panel to increase or decrease the number of terms automatically displayed or to completely turn that feature off. Global warming is real and human-caused. It is leading to large-scale climate change. Under the guise of climate "skepticism", the public is bombarded with misinformation that casts doubt on the reality of human-caused global warming. This website gets skeptical about global warming "skepticism". Our mission is simple: debunk climate misinformation by presenting peer-reviewed science and explaining the techniques of science denial. The next iteration of our free online course, Making Sense of Climate Science Denial , starts on February 8 and it will be the 15th run since the very first one in April

expected to operate continuously without planned downtime for their entire service lives. non-disruPtive everything. The developers designed hardware that.

Holistic Privacy-Preserving Identity Management System for the Internet of Things

It has been a while now since cryptocurrencies and blockchain have been defined as transformative for finance and many more industries. One of the biggest challenges that most cryptocurrency holders face today is handling of private keys. Private keys are bits of data that give users ownership of their coins and enable them to make cryptocurrency payments.


A system, apparatus, method, and machine readable medium are described for sharing authentication data. This invention relates generally to the field of data processing systems. More particularly, the invention relates to advanced user authentication techniques and associated applications. When operated normally, a biometric sensor reads raw biometric data from the user e. A matcher module compares the extracted features with biometric reference data stored in a secure storage on the client and generates a score based on the similarity between the extracted features and the biometric reference data The biometric reference data is typically the result of an enrollment process in which the user enrolls a fingerprint, voice sample, image or other biometric data with the device

In the nascent area of cryptocurrency and transactions of virtual assets, a new type of entity is slowly emerging called the Virtual Asset Service Providers VASP. The most common example of a VASP today is the cryptocurrency exchanges, which support end-users in delivering cryptocurrencies e.

Jorge Bernal Bernabe, Jose L. Hernandez-Ramos, Antonio F. Security and privacy concerns are becoming an important barrier for large scale adoption and deployment of the Internet of Things. To address this issue, the identity management system defined herein provides a novel holistic and privacy-preserving solution aiming to cope with heterogeneous scenarios that requires both traditional online access control and authentication, along with claim-based approach for M2M machine to machine interactions required in IoT. This symbiosis endows the IdM system with advanced features such as privacy-preserving, minimal disclosure, zero-knowledge proofs, unlikability, confidentiality, pseudonymity, strong authentication, user consent, and offline M2M transactions.

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