Casting Light on the Dark Web: A Guide for Safe Exploration - Couverture rigide

Beckstrom, Matthew; Lund, Brady

Couverture rigide

ISBN 10 : 1538120933 ISBN 13 : 9781538120934

Editeur : Rowman & Littlefield, 2019

Synopsis

Covers topics from what the dark web is, to how it works, to how you can use it, to some of the myths surrounding it.

Casting Light on the Dark Web: A Guide for Safe Exploration is an easy-to-read and comprehensive guide to understanding how the Dark Web works and why you should be using it! Readers will be led on a tour of this elusive technology from how to download the platform for personal or public use, to how it can best be utilized for finding information. This guide busts myths and informs readers, while remaining jargon-free and entertaining. Useful for people of all levels of internet knowledge and experience.

A propos du livre

À propos des auteurs

Matthew Beckstrom was born and raised in Montana. He received an Associate's degree in Computer Science from the University of Montana, and then later, a Bachelor's degree in Computer Science from Montana State University. After working in various technology jobs, he finally settled into the job of systems manager at the Lewis & Clark Library in Helena in 1999. In 2012, he received his Master's degree in Information Science from the University of North Texas after receiving a grant from the Montana State Library to attend library school.

Brady Lund, Ph.D., is an assistant professor of information science at the University of North Texas. He has published four books related to technology in libraries and educational institutions - including Casting Light on the Dark Web and Creating Accessible Online Instruction Using Universal Design Principles, both for Rowman and Littlefield Publishing - and nearly 100 articles, editorials, and opinion papers. His work often combines data analytics principles with library and information science research topics. Daniel Agbaji is a Ph.D. student in information science at the University of North Texas, with a major in Data Science-Artificial Intelligence and Machine Learning. As an experienced researcher and software developer, he has written scholarly publications and book chapters with notable publishers. Daniel has published articles in the information science and library field. As a software developer, Daniel has written thousands of lines of code for fortune 500 companies which are not publicly available due to company policies.

Détails de l'édition

• Éditeur: Rowman & Littlefield
• Date d'édition: 2019
• Langue: anglais
• ISBN 10: 1538120933
• ISBN 13: 9781538120934
• Reliure: Relié
• Nombre de pages: 132
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible