Policy-Based Autonomic Data Governance / edited by Seraphin Calo, Elisa Bertino, Dinesh Verma.

Format:

Book

Contributor:

Calo, Seraphin., Editor.

Bertino, Elisa, Editor.

Verma, Dinesh, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 11550

Information Systems and Applications, incl. Internet/Web, and HCI ; 11550

Language:

English

Subjects (All):

Computer security.

Computer networks.

Artificial intelligence.

Computers.

Principles and Models of Security.

Computer Communication Networks.

Artificial Intelligence.

Computer Hardware.

Local Subjects:

Principles and Models of Security.

Computer Communication Networks.

Artificial Intelligence.

Computer Hardware.

Physical Description:

1 online resource (XIV, 227 pages) : 126 illustrations, 36 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

Advances in artificial intelligence, sensor computing, robotics, and mobile systems are making autonomous systems a reality. At the same time, the influence of edge computing is leading to more distributed architectures incorporating more autonomous elements. The flow of information is critical in such environments, but the real time, distributed nature of the system components complicates the data protection mechanisms. Policy-based management has proven useful in simplifying the complexity of management in domains like networking, security, and storage; it is expected that many of those benefits would carry over to the task of managing big data and autonomous systems. This book aims at providing an overview of recent work and identifying challenges related to the design of policy-based approaches for managing big data and autonomous systems. An important new direction explored in the book is to make the major elements of the system self-describing and self-managing. This would lead to architectures where policy mechanisms are tightly coupled with the system elements. In such integrated architectures, we need new models for information assurance, traceability of information, and better provenance on information flows. In addition when dealing with devices with actuation capabilities and, thus, being able to make changes to physical spaces, safety is critical. With an emphasis on policy-based mechanisms for governance of data security and privacy, and for safety assurance, the papers in this volume follow three broad themes: foundational principles and use-cases for the autonomous generation of policies; safe autonomy; policies and autonomy in federated environments.

Contents:

Systems, use-cases and foundational principles underlying generative policies

AGENP: An ASGrammar-based GENerative Policy Framework

Value of Information: Quantification and Application to Coalition Machine Learning

Self-Generating Policies for Machine Learning in Coalition Environments

Approaches and techniques for safe autonomy

Can N-version Decision-Making Prevent the Rebirth of HAL 9000 in Military Camo? Using a "Golden Rule" Threshold to Prevent AI Mission Individuation

Simulating User Activity for Assessing Effect of Sampling on DB Activity Monitoring Anomaly Detection

FADa-CPS - Faults and Attacks Discrimination in Cyber Physical Systems

Techniques and Systems for Anomaly Detection in Database Systems

Policies and autonomy in federated and distributed environments

Towards Enabling trusted Artificial Intelligence via Blockchain

Secure Model Fusion for Distributed Learning using Partial Homomorphic Encryption

Policy based Identification of IoT Devices' Vendor and Type by DNS Traffic Analysis

Redundancy for the Autonomic Management of Fault-tolerance in the Internet of Things.

Other Format:

Printed edition:

ISBN:

978-3-030-17277-0

9783030172770

Access Restriction:

Restricted for use by site license.