Dynamic Data Sharing For Public Data Integrity Auditing In The Cloud

Public data integrity auditing in the cloud environment is a critical concern to ensure the trustworthiness and reliability of shared data. With the increasing adoption of cloud computing, where data is stored and processed in remote servers, there is a need for robust mechanisms to verify the integrity of public data and detect any unauthorized modifications. One approach to address this challenge is the dynamic data sharing process. It involves enabling multiple users or organizations to access and verify the integrity of shared data in a collaborative manner. By allowing dynamic data sharing, users can collectively participate in the auditing process, contributing their computing resources and expertise to ensure data integrity. In this process, cryptographic techniques and distributed algorithms are often employed. Cryptographic hashes or digital signatures are used to generate integrity proofs for the data, which can be shared and verified by multiple participants. Distributed algorithms distribute the auditing tasks across different nodes in the cloud, improving efficiency and scalability. The dynamic data sharing approach offers several benefits. Firstly, it enhances transparency and accountability by involving multiple parties in the auditing process. Secondly, it improves the resilience of the auditing system, as multiple participants can detect and mitigate attacks or errors. Thirdly, it enables real-time auditing, allowing prompt detection of any integrity violations and timely remediation. By leveraging dynamic data sharing for public data integrity auditing in the cloud, organizations and users can have greater confidence in the integrity of their shared data. This can be particularly useful in scenarios involving sensitive or critical data, such as government records, financial transactions, or healthcare information. Ensuring data integrity in the cloud environment is essential for maintaining trust, protecting privacy, and upholding the credibility of public data.

Many Cloud data auditing algorithms have been proposed to maintain the integrity and privacy of data held in the Cloud. In this book, we present a survey of the state of the art and research of Cloud data auditing techniques with a brief introduction of the basic cloud computing concepts, its architecture and security issues. This book presents an overview of the various methods presently used to perform Cloud data auditing, mostly focusing on integrity and privacy.

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.

This book highlights the latest research findings, innovative research results, methods and development techniques, from both theoretical and practical perspectives, in the emerging areas of information networking, data and Web technologies. It gathers papers originally presented at the 5th International Conference on Emerging Internetworking, Data & Web Technologies (EIDWT-2017) held 10–11 June 2017 in Wuhan, China. The conference is dedicated to the dissemination of original contributions that are related to the theories, practices and concepts of emerging internetworking and data technologies – and most importantly, to how they can be applied in business and academia to achieve a collective intelligence approach. Information networking, data and Web technologies are currently undergoing a rapid evolution. As a result, they are now expected to manage increasing usage demand, provide support for a significant number of services, consistently deliver Quality of Service (QoS), and optimize network resources. Highlighting these aspects, the book discusses methods and practices that combine various internetworking and emerging data technologies to capture, integrate, analyze, mine, annotate, and visualize data, and make it available for various users and applications.

This book presents best selected research papers presented at the 3rd International Conference on Cognitive Informatics and Soft Computing (CISC 2020), held at Balasore College of Engineering & Technology, Balasore, Odisha, India, from 12 to 13 December 2020. It highlights, in particular, innovative research in the fields of cognitive informatics, cognitive computing, computational intelligence, advanced computing, and hybrid intelligent models and applications. New algorithms and methods in a variety of fields are presented, together with solution-based approaches. The topics addressed include various theoretical aspects and applications of computer science, artificial intelligence, cybernetics, automation control theory, and software engineering.

This book constitutes the refereed proceedings of the Second International Conference on Smart Trends in Information Technology and Computer Communications, SmartCom 2017, held in Pune, India, in August 2017. The 38 revised papers presented were carefully reviewed and selected from 310 submissions. The papers address issues on smart and secure systems; smart and service computing; smart data and IT innovations.

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

The two-volume set LNCS 9722 and LNCS 9723 constitutes the refereed proceedings of the 21st Australasian Conference on Information Security and Privacy, ACISP 2016, held in Melbourne, VIC, Australia, in July 2016. The 52 revised full and 8 short papers presented together with 6 invited papers in this double volume were carefully revised and selected from 176 submissions. The papers of Part I (LNCS 9722) are organized in topical sections on National Security Infrastructure; Social Network Security; Bitcoin Security; Statistical Privacy; Network Security; Smart City Security; Digital Forensics; Lightweight Security; Secure Batch Processing; Pseudo Random/One-Way Function; Cloud Storage Security; Password/QR Code Security; and Functional Encryption and Attribute-Based Cryptosystem. Part II (LNCS 9723) comprises topics such as Signature and Key Management; Public Key and Identity-Based Encryption; Searchable Encryption; Broadcast Encryption; Mathematical Primitives; Symmetric Cipher; Public Key and Identity-Based Encryption; Biometric Security; Digital Forensics; National Security Infrastructure; Mobile Security; Network Security; and Pseudo Random/One-Way Function.