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Author: Wen Ming Liu
Publisher: Springer
ISBN: 3319426443
Category : Computers
Languages : en
Pages : 154
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Book Description
This book offers a novel approach to data privacy by unifying side-channel attacks within a general conceptual framework. This book then applies the framework in three concrete domains. First, the book examines privacy-preserving data publishing with publicly-known algorithms, studying a generic strategy independent of data utility measures and syntactic privacy properties before discussing an extended approach to improve the efficiency. Next, the book explores privacy-preserving traffic padding in Web applications, first via a model to quantify privacy and cost and then by introducing randomness to provide background knowledge-resistant privacy guarantee. Finally, the book considers privacy-preserving smart metering by proposing a light-weight approach to simultaneously preserving users' privacy and ensuring billing accuracy. Designed for researchers and professionals, this book is also suitable for advanced-level students interested in privacy, algorithms, or web applications.
Author: Wen Ming Liu
Publisher: Springer
ISBN: 3319426443
Category : Computers
Languages : en
Pages : 154
Get Book Here
Book Description
This book offers a novel approach to data privacy by unifying side-channel attacks within a general conceptual framework. This book then applies the framework in three concrete domains. First, the book examines privacy-preserving data publishing with publicly-known algorithms, studying a generic strategy independent of data utility measures and syntactic privacy properties before discussing an extended approach to improve the efficiency. Next, the book explores privacy-preserving traffic padding in Web applications, first via a model to quantify privacy and cost and then by introducing randomness to provide background knowledge-resistant privacy guarantee. Finally, the book considers privacy-preserving smart metering by proposing a light-weight approach to simultaneously preserving users' privacy and ensuring billing accuracy. Designed for researchers and professionals, this book is also suitable for advanced-level students interested in privacy, algorithms, or web applications.
Author: John R. Vacca
Publisher: CRC Press
ISBN: 1482260956
Category : Computers
Languages : en
Pages : 519
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Book Description
This handbook offers a comprehensive overview of cloud computing security technology and implementation, while exploring practical solutions to a wide range of cloud computing security issues. With more organizations using cloud computing and cloud providers for data operations, proper security in these and other potentially vulnerable areas have become a priority for organizations of all sizes across the globe. Research efforts from both academia and industry in all security aspects related to cloud computing are gathered within one reference guide.
Author: Kwangjo Kim
Publisher: Springer Nature
ISBN: 9811637644
Category : Computers
Languages : en
Pages : 81
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Book Description
This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
Author: Keesook J. Han
Publisher: Springer Science & Business Media
ISBN: 1461432960
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
This book mainly focuses on cloud security and high performance computing for cloud auditing. The book discusses emerging challenges and techniques developed for high performance semantic cloud auditing, and presents the state of the art in cloud auditing, computing and security techniques with focus on technical aspects and feasibility of auditing issues in federated cloud computing environments. In summer 2011, the United States Air Force Research Laboratory (AFRL) CyberBAT Cloud Security and Auditing Team initiated the exploration of the cloud security challenges and future cloud auditing research directions that are covered in this book. This work was supported by the United States government funds from the Air Force Office of Scientific Research (AFOSR), the AFOSR Summer Faculty Fellowship Program (SFFP), the Air Force Research Laboratory (AFRL) Visiting Faculty Research Program (VFRP), the National Science Foundation (NSF) and the National Institute of Health (NIH). All chapters were partially supported by the AFOSR Information Operations and Security Program extramural and intramural funds (AFOSR/RSL Program Manager: Dr. Robert Herklotz). Key Features: · Contains surveys of cyber threats and security issues in cloud computing and presents secure cloud architectures · Presents in-depth cloud auditing techniques, federated cloud security architectures, cloud access control models, and access assured information sharing technologies · Outlines a wide range of challenges and provides solutions to manage and control very large and complex data sets
Author: Raheem Beyah
Publisher: Springer
ISBN: 303001701X
Category : Computers
Languages : en
Pages : 617
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Book Description
This two-volume set LNICST 254-255 constitutes the post-conference proceedings of the 14thInternational Conference on Security and Privacy in Communication Networks, SecureComm 2018, held in Singapore in August 2018. The 33 full and 18 short papers were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on IoT security, user and data privacy, mobile security, wireless security, software security, cloud security, social network and enterprise security, network security, applied cryptography, and web security.
Author: Wei Xiang
Publisher: Springer Nature
ISBN: 3030934799
Category : Computers
Languages : en
Pages : 348
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Book Description
This book constitutes the refereed post-conference proceedings of the 12th International Conference on Broadband Communications, Networks, and Systems, Broadnets 2021, which took place in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 24 full papers presented were carefully reviewed and selected from 49 submissions. The papers are thematically grouped as a session on broadband communications, networks, and systems; 5G-enabled smart building: technology and challenge; and 5G: The advances in industry.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309465370
Category : Social Science
Languages : en
Pages : 195
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Book Description
The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.
Author: Rohit Anand
Publisher: CRC Press
ISBN: 1000906108
Category : Computers
Languages : en
Pages : 376
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Book Description
Integration of IoT with Cloud Computing for Smart Applications provides an integrative overview of the Internet of Things (IoT) and cloud computing to be used for the various futuristic and intelligent applications. The aim of this book is to integrate IoT and cloud computing to translate ordinary resources into smart things. Discussions in this book include a broad and integrated perspective on the collaboration, security, growth of cloud infrastructure, and real-time data monitoring. Features: Presents an integrated approach to solve the problems related to security, reliability, and energy consumption. Explains a unique approach to discuss the research challenges and opportunities in the field of IoT and cloud computing. Discusses a novel approach for smart agriculture, smart healthcare systems, smart cities and many other modern systems based on machine learning, artificial intelligence, and big data, etc. Information presented in a simplified way for students, researchers, academicians and scientists, business innovators and entrepreneurs, management professionals and practitioners. This book can be great reference for graduate and postgraduate students, researchers, and academicians working in the field of computer science, cloud computing, artificial intelligence, etc.
Author: Uma Maheswari V
Publisher: CRC Press
ISBN: 1040109675
Category : Computers
Languages : en
Pages : 318
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Book Description
This book covers the fundamental concepts of private AI and its applications. It also covers fusion of Private AI with cutting-edge technologies like cloud computing, federated learning and computer vision. Security Models and Applications for Sustainable Development Using Private AI reviews various encryption algorithms used for providing security in private AI. It discusses the role of training machine learning and Deep learning technologies in private AI. The book provides case studies of using private AI in various application areas such as purchasing, education, entertainment, medical diagnosis, predictive care, conversational personal assistants, wellness apps, early disease detection, and recommendation systems. The authors provide additional knowledge to handling the customer’s data securely and efficiently. It also provides multi-model dataset storage approaches along with the traditional approaches like anonymization of data and differential privacy mechanisms. The target audience includes undergraduate and postgraduate students in Computer Science, Information technology, Electronics and Communication Engineering and related disciplines. This book is also a one stop reference point for professionals, security researchers, scholars, various government agencies and security practitioners, and experts working in the cybersecurity Industry specifically in the R & D division.
Author: Joaquin Garcia-Alfaro
Publisher: Springer
ISBN: 3319298836
Category : Computers
Languages : en
Pages : 298
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Book Description
This book constitutes the revised selected papers of the 10th International Workshop on Data Privacy Management, DPM 2015, and the 4th International Workshop on Quantitative Aspects in Security Assurance, QASA 2015, held in Vienna, Austria, in September 2015, co-located with the 20th European Symposium on Research in Computer Security, ESORICS 2015. In the DPM 2015 workshop edition, 39 submissions were received. In the end, 8 full papers, accompanied by 6 short papers, 2 position papers and 1 keynote were presented in this volume. The QASA workshop series responds to the increasing demand for techniques to deal with quantitative aspects of security assurance at several levels of the development life-cycle of systems and services, from requirements elicitation to run-time operation and maintenance. QASA 2015 received 11 submissions, of which 4 papers are presented in this volume as well.
