Classification of File Data Based on Confidentiality in Cloud Computing using K-NN Classifier

2016 ◽  
Vol 3 (2) ◽  
pp. 61-78 ◽  
Author(s):  
Munwar Ali Zardari ◽  
Low Tang Jung
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Virtual Machine ◽  
Data Security ◽  
Virtual Machines ◽  
Data Classification ◽  
Data Encryption ◽  
Cloud Services ◽  
Security Level ◽  
Public Data

Cloud computing is a new paradigm model that offers different services to its customers. The increasing number of users for cloud services i.e. software, platform or infrastructure is one of the major reasons for security threats for customers' data. Some major security issues are highlighted in data storage service in the literature. Data of thousands of users are stored on a single centralized place where the possibility of data threat is high. There are many techniques discussed in the literature to keep data secure in the cloud, such as data encryption, private cloud and multiple clouds concepts. Data encryption is used to encrypt the data or change the format of the data into the unreadable format that unauthorized users could not understand even if they succeed to get access of the data. Data encryption is very expensive technique, it takes time to encrypt and decrypt the data. Deciding the security approach for data security without understanding the security needs of the data is a technically not a valid approach. It is a basic requirement that one should understand the security level of data before applying data encryption security approach. To discover the data security level of the data, the authors used machine learning approach in the cloud. In this paper, a data classification approach is proposed for the cloud and is implemented in a virtual machine named as Master Virtual Machine (Vmm). Other Vms are the slave virtual machines which will receive from Vmm the classified information for further processing in cloud. In this study the authors used three (3) virtual machines, one master Vmm and two slaves Vms. The master Vmm is responsible for finding the classes of the data based on its confidentiality level. The data is classified into two classes, confidential (sensitive) and non-confidential (non-sensitive/public) data using K-NN algorithm. After classification phase, the security phase (encryption phase) shall encrypt only the confidential (sensitive) data. The confidentiality based data classification is using K-NN in cloud virtual environment as the method to encrypt efficiently the only confidential data. The proposed approach is efficient and memory space friendly and these are the major findings of this work.

Classification of File Data Based on Confidentiality in Cloud Computing Using K-NN Classifier

2019 ◽  
pp. 678-697
Author(s):  
Munwar Ali Zardari ◽  
Low Tang Jung
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Data Security ◽  
Virtual Machines ◽  
Data Classification ◽  
Data Encryption ◽  
Security Level ◽  
New Paradigm ◽  
Memory Space ◽  
Multiple Clouds

Cloud computing is a new paradigm model that offers different services to its customers. The increasing number of users for cloud services i.e. software, platform or infrastructure is one of the major reasons for security threats for customers' data. Some major security issues are highlighted in data storage service in the literature. Data of thousands of users are stored on a single centralized place where the possibility of data threat is high. There are many techniques discussed in the literature to keep data secure in the cloud, such as data encryption, private cloud and multiple clouds concepts. Data encryption is used to encrypt the data or change the format of the data into the unreadable format that unauthorized users could not understand even if they succeed to get access of the data. Data encryption is very expensive technique, it takes time to encrypt and decrypt the data. Deciding the security approach for data security without understanding the security needs of the data is a technically not a valid approach. It is a basic requirement that one should understand the security level of data before applying data encryption security approach. To discover the data security level of the data, the authors used machine learning approach in the cloud. In this paper, a data classification approach is proposed for the cloud and is implemented in a virtual machine named as Master Virtual Machine (Vmm). Other Vms are the slave virtual machines which will receive from Vmm the classified information for further processing in cloud. In this study the authors used three (3) virtual machines, one master Vmm and two slaves Vms. The master Vmm is responsible for finding the classes of the data based on its confidentiality level. The data is classified into two classes, confidential (sensitive) and non-confidential (non-sensitive/public) data using K-NN algorithm. After classification phase, the security phase (encryption phase) shall encrypt only the confidential (sensitive) data. The confidentiality based data classification is using K-NN in cloud virtual environment as the method to encrypt efficiently the only confidential data. The proposed approach is efficient and memory space friendly and these are the major findings of this work.

A Study on Cloud Storage Security Method Using Data Classification

2021 ◽  
Vol 23 (09) ◽  
pp. 1105-1121
Author(s):  
Dr. Ashish Kumar Tamrakar ◽  
◽  
Dr. Abhishek Verma ◽  
Dr. Vishnu Kumar Mishra ◽  
Dr. Megha Mishra ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Storage Systems ◽  
Data Classification ◽  
Data Encryption ◽  
Security Requirements ◽  
Security And Privacy ◽  
Security Level ◽  
Distributed Resources ◽  
Storage Security ◽  
Public Data

Cloud computing is a new model for providing diverse services of software and hardware. This paradigm refers to a model for enabling on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal service provider interaction .It helps the organizations and individuals deploy IT resources at a reduced total cost. However, the new approaches introduced by the clouds, related to computation outsourcing, distributed resources and multi-tenancy concept, increase the security and privacy concerns and challenges. It allows users to store their data remotely and then access to them at any time from any place .Cloud storage services are used to store data in ways that are considered cost saving and easy to use. In cloud storage, data are stored on remote servers that are not physically known by the consumer. Thus, users fear from uploading their private and confidential files to cloud storage due to security concerns. The usual solution to secure data is data encryption, which makes cloud users more satisfied when using cloud storage to store their data. Motivated by the above facts; we have proposed a solution to undertake the problem of cloud storage security. In cloud storage, there are public data that do not need any security measures, and there are sensitive data that need applying security mechanisms to keep them safe. In that context, data classification appears as the solution to this problem. The classification of data into classes, with different security requirements for each class is the best way to avoid under security and over security situation. The existing cloud storage systems use the same Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 9, September – 2021 Page-1105 key size to encrypt all data without taking into consideration its confidentiality level. Treating the low and high confidential data with the same way and at the same security level will add unnecessary overhead and increase the processing time. In our proposal, we have combined the K-NN (K Nearest Neighbors) machine learning method and the goal programming decision-making method, to provide an efficient method for data classification. This method allows data classification according to the data owner security needs. Then, we introduce the user data to the suitable security mechanisms for each class. The use of our solution in cloud storage systems makes the data security process more flexible, besides; it increases the cloud storage system performance and decreases the needed resources, which are used to store the data.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

scholarly journals Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Service Provider

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.

Optimal Green Virtual Machine Migration Model

2013 ◽  
Vol 9 (3) ◽  
pp. 35-52 ◽  
Author(s):  
Noah Sabry ◽  
Paul Krause
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Carbon Footprint ◽  
Virtual Machines ◽  
Clean Energy ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Migration Model ◽  
Data Centres

Cloud computing provides the opportunity to migrate virtual machines to “follow-the-green” data centres. That is, to migrate virtual machines between green data centres on the basis of clean energy availability, to mitigate the environmental impact of carbon footprint emissions and energy consumption. The virtual machine migration problem can be modelled to maximize the utility of computing resources or minimizing the cost of using computing resources. However, this would ignore the network energy consumption and its impact on the overall CO2 emissions. Unless this is taken into account the extra data traffic due to migration of data could then cause an increase in brown energy consumption and eventually lead to an unintended increase in carbon footprint emissions. Energy consumption is a key aspect in deploying distributed service in cloud networks within decentralized service delivery architectures. In this paper, the authors address an optimization view of the problem of locating a set of cloud services on a set of sites green data centres managed by a service provider or hybrid cloud computing brokerage. The authors’ goal is to minimize the overall network energy consumption and carbon footprint emissions for accessing the cloud services for any pair of data centres i and j. The authors propose an optimization migration model based on the development of integer linear programming (ILP) models, to identify the leverage of green energy sources with data centres and the energy consumption of migrating VMs.

scholarly journals Privacy Preservation of a Group and Secure Data Storage in Cloud Environment

2015 ◽  
Vol 15 (1) ◽  
pp. 46-54
Author(s):  
K. Govinda ◽  
E. Sathiyamoorthy
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Privacy Preservation ◽  
Personal Information ◽  
Cloud Service ◽  
Cloud Services ◽  
Security And Privacy ◽  
Cloud Environment ◽  
Secure Data

Abstract Cloud computing has become a victorious archetype for data storage, as well as for computation purposes. Greater than ever it concerns user’s privacy, so that data security in a cloud is increasing day by day. Ensuring security and privacy for data organization and query dispensation in the cloud is important for superior and extended uses of cloud based technologies. Cloud users can barely have the full benefits of cloud computing if we can ensure the real user’s privacy and his data security concerns this approach along with storing thin-skinned personal information in databases and software spread around the cloud. There are numerous service suppliers in WWW (World Wide Web), who can supply each service as a cloud. These cloud services will switch over data with a supplementary cloud, so that when the data is exchanged between the clouds, the problem of confidentiality revelation exists. So the privacy revelation problem concerning a person or a corporation is unavoidably open when releasing or data distributing in the cloud service. Confidentiality is a significant issue in any cloud computing environment. In this paper we propose and implement a mechanism to maintain privacy and secure data storage for group members or a community in cloud environment.

scholarly journals A Comprehensive Overview of Privacy and Data Security for Cloud Storage

2021 ◽  
pp. 113-152
Author(s):  
Dr. Nikhat Akhtar ◽  
Dr. Bedine Kerim ◽  
Dr. Yusuf Perwej ◽  
Dr. Anurag Tiwari ◽  
Dr. Sheeba Praveen
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Cloud Storage ◽  
Low Cost ◽  
Network Connectivity ◽  
Data Encryption ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Resource Pooling

People used to carry their documents about on CDs only a few years ago. Many people have recently turned to memory sticks. Cloud computing, in this case, refers to the capacity to access and edit data stored on remote servers from any Internet-connected platform. Cloud computing is a self-service Internet infrastructure that allows people to access computing resources at any location worldwide. The world has altered as a result of cloud computing. Cloud computing can be thought of as a new computing typology that can provide on-demand services at a low cost. By increasing the capacity and flexibility of data storage and providing scalable compute and processing power that fits the dynamic data requirements, cloud computing has aided the advancement of IT to higher heights. In the field of information technology, privacy and data security have long been a serious concern. It becomes more severe in the cloud computing environment because data is stored in multiple locations, often across the globe. Users' primary challenges regarding the cloud technology revolve around data security and privacy. We conduct a thorough assessment of the literature on data security and privacy issues, data encryption technologies, and related countermeasures in cloud storage systems in this study. Ubiquitous network connectivity, location-independent resource pooling, quick resource flexibility, usage-based pricing, and risk transference are all features of cloud computing.

scholarly journals A Secure Data Storage Strategy for Private Cloud

2020 ◽  
Vol 9 (6) ◽  
pp. 1043-1049
Keyword(s):  
Data Storage ◽  
Data Security ◽  
Key Management ◽  
Data Encryption ◽  
Cloud Services ◽  
Third Party ◽  
Widespread Application ◽  
Trusted Third Party ◽  
Cloud Data ◽  
Security Issues

In recent years, with the widespread application of cloud computing, more and more enterprises, institutions, and individuals have started to use cloud services to place their data in the cloud. With the rise of cloud services, the accompanying data security issues have received increasing attention. Because data stores are in the cloud, there are many outstanding security issues. This paper proposes a public cloud data security solution based on a trusted third-party platform. The solution is based on an independent and trusted third-party platform, and has certain advantages in data encryption, key management, data awareness, data sharing, and accident responsibility.

scholarly journals Role of Data Hiding Techniques for Detection of Virtual Disks in Cloud Environment

2020 ◽  
Vol 8 (5) ◽  
pp. 151-156
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Data Hiding ◽  
Service Provider ◽  
Data Center ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Environment ◽  
Complete Control ◽  
Public Data

In today's era, cloud computing is very popular and the most widly used technique to store the data. As we know more than 75% of the data that is used in internet services and applications is being stored on the maximum cloud only. Where our data is stored in the cloud, it is called data center, there are two important roles in cloud computing technology, one is cloud customer and the other is cloud service provider. The complete control and monitoring at the public data center is of the service provider itself, the user is kept away from the information of the location of the data center and its access credentials. This means that the user has absolutely no information about the virtual machine hard disk, and their access locations. Whenever any forensic inquiry comes in the cloud environment, the Investigator and forensic expert first have to find out about the virtual machine disk and its location in the cloud, which is a very challenging and difficult task in the cloud environment. In this paper we have developed a new process that detects virtual machines using data hiding techniques. To prove this new algorithm, we have performed an experiment using Oracle VirtualBox 6.0 on OpenSUSE virtual machine.

scholarly journals OB-MECC: An Efficient Confidentiality and Security Enhancement for Cloud Storage System

2021 ◽  
Author(s):  
P. NagaRaju ◽  
N. Nagamalleswara Rao
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Cloud Services ◽  
Security Level ◽  
Data Confidentiality ◽  
Security Enhancement ◽  
Reducing Costs ◽  
Main Barrier

Cloud computing (CC) is one amongst the developing technologies, which get more attention from academia as well as industries. It offers diverse benefits like sharing computing resources, service flexibility, reducing costs, etc. The Cloud Services Provider (CSP) is accountable for the data that are delivered to the cloud. The threat of seeing the stored data and using sensitive raw data by strangers is the main barrier in the utilization of cloud services. So, Data Security (DS) along with privacy is the chief issue, which is an obstacle while adopting the CC. Countless techniques are existent for ensuring data confidentiality, but they do not completely give protection to the data. To trounce these drawbacks, this paper introduces the Obfuscation (OB) based Modified Elliptical Curve Cryptography (MECC) algorithm for protecting data as of malicious attacks, which is termed as OB-MECC. Primarily, the proposed method obfuscates the data before they are uploaded to the cloud. For the OB of the data, the proposed work employs methods like substitution cipher (SC), position update, Ceaser cipher, binary conversion, 8-bit binary conversion, decimal(),  two complex(), and ASCII(). Then, encryption of the obfuscated data is done with the utilization of the MECC algorithm. After encryption, the data on the cloud is retrieved. The retrieved data is then decrypted by reversing the OB and encryption process to get the actual data. The outcomes corroborate that the confidentiality and security level are maximum for the proposed OB-MECC when contrasted to the existing approaches.

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