Privacy Preservation Using Novel Identity Management Scheme in Cloud Computing

2015 ◽  
Author(s):  
Dishant Soni ◽  
Hiren Patel
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Identity Management ◽  
Management Scheme

A Cloud Privacy-Preserving Model Using Gaussian Mutation-Based Bacterial Foraging Optimization and Genetic Crossover Operation

2021 ◽  
Author(s):  
K Anand ◽  
A. Vijayaraj ◽  
M. Vijay Anand
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Cost Savings ◽  
Cloud Security ◽  
Public Access ◽  
Key Generation ◽  
Gaussian Mutation ◽  
Bacterial Foraging Optimization ◽  
Sensitive Data ◽  
Bacterial Foraging

Abstract The necessity of security in the cloud system increases day by day in which the data controllers harvest the rising personal and sensitive data volume.The cloud has some unprotected private data as well as data that has been outsourced for public access, which is crucial for cloud security statements. An advanced legal data protection constraint is required due to the resultant of repeated data violations. While dealing with sensitive data, most of the existing techniques failed to handle optimal privacy and different studies were performed to take on cloud privacy preservation. Hence, the novel model of privacy preservation in the cloud and artificial intelligence (AI) techniques were used to tackle these challenges. These AI methods are insight-driven, strategic, and more efficient organizations in cloud computing. However, the cost savings, agility, higher flexibility businesses are offered with cloud computing by data hosting. Data cleansing and restoration are the two major steps involved in the proposed privacy replica. In this study, we proposed Chaotic chemotaxis and Gaussian mutation-based Bacterial Foraging Optimization with genetic crossover operation (CGBFO- GC) algorithm for optimal key generation. Deriving the multi-objective function parameters namely data preservation ratio, hiding ratio, and modification degree that accomplishes optimal key generation using CGBFO- GC algorithm. Ultimately, the proposed CGBFO- GC algorithm provides more efficient performance results in terms of cloud security than an existing method such as SAS-DPSO, CDNNCS, J-SSO, and GC.

A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 378-389
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

An Unified Secured Cloud System for the Education Sector of India

2020 ◽  
pp. 69-102
Author(s):  
Kimaya Arun Ambekar ◽  
Kamatchi R.
Keyword(s):  
Cloud Computing ◽  
Network Security ◽  
Access Control ◽  
Identity Management ◽  
Data Encryption ◽  
Cloud Services ◽  
Education Sector ◽  
Cloud Environment ◽  
The World ◽  
Community Cloud

Cloud computing is based on years of research on various computing paradigms. It provides elasticity, which is useful in the situations of uneven ICT resources demands. As the world is moving towards digitalization, the education sector is expected to meet the pace. Acquiring and maintaining the ICT resources also necessitates a huge amount of cost. Education sector as a community can use cloud services on various levels. Though the cloud is very successfully running technology, it also shows some flaws in the area of security, privacy and trust. The research demonstrates a model in which major security areas are covered like authorization, authentication, identity management, access control, privacy, data encryption, and network security. The total idea revolves around the community cloud as university at the center and other associated colleges accessing the resources. This study uses OpenStack environment to create a complete cloud environment. The validation of the model is performed using some cases and some tools.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 840-856
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

An Extensible Identity Management Framework for Cloud-Based E-Government Systems

2016 ◽  
pp. 399-422
Author(s):  
Hirra Anwar ◽  
Muhammad Awais Shibli ◽  
Umme Habiba
Keyword(s):  
Cloud Computing ◽  
Identity Management ◽  
Security Requirements ◽  
Computing Technology ◽  
Management Framework ◽  
Government Organizations ◽  
Domain Specific ◽  
Government Institutions ◽  
Design And Implementation

Numerous Cloud Identity Management (IdM) systems have been designed and implemented to meet the diverse functional and security requirements of various organizations. These requirements are subjective in nature; for instance, some government organizations require security more than efficiency while others prioritize performance and immediate response over security. However, most of the existing IdM systems are incapable of handling the user-centricity, security & technology requirements and are also domain specific. In this regard, this chapter elaborates the need to use Cloud Computing technology for enhancing the effectiveness and transparency of IdM functions and presents a comprehensive and well-structured Extensible IdM Framework for Cloud based e-government institutions. We present the design and implementation details of the proposed framework, followed by a case study which shows how government organizations of Pakistan would use the proposed framework to improve their IdM processes and achieve diverse IdM services.

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