Design and Implementation of a Secure Multi-cloud Data Storage System
Chapter One
Research Objectives
General Objective
The general objective of this study was to investigate a multi-cloud data storage security system that addresses performance, security, and reliability challenges within the cloud computing domain. The study aimed to design a model for securely storing data across multiple cloud environments.
Specific Objectives
The specific objectives of this study were:
- To explore various approaches to secure and robust data storage in multi-cloud environments.
2. To propose a controlled approach to mitigate security issues faced by end users of cloud services.
3. To design a multi-cloud data storage model that enables organizations to securely encrypt and distribute their data across multiple cloud providers.
- To evaluate the effectiveness of the multi-cloud storage model in enhancing data security, reliability, and performance in cloud computing.
CHAPTER TWO
LITERATURE REVIEW
Introduction.
In this chapter, the research focuses on theoretical framework, existing theories relating to the impact of employing various data storage systems towards robust data storage in wireless sensor networks. The research will mainly focus on Cloud data storage.
Wireless Sensor Network
A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes.
Most of the research done in the field of wireless sensor networks focused in-network support and disregard the backend that has to deal with the immense storing and processing requirements.
This section shows that although there are studies involving the integration of WSN with the Cloud, transparent integration with heterogeneous Cloud computing systems is still a recent topic.
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Wireless sensor networks are becoming increasingly common and we believe they are an important part of the future of machine-to-machine communication and the Internet of Things.(Wan & Zou, 2013)
Nowadays, acquiring the required infrastructure and programming the backend to deal with all the requisites of a WSN is a cumbersome task at best. (Conzon &Brizzi, 2015)
The best way of implementing the backend is to take advantage of the emerging Cloud Computing paradigm. The future state described as the Internet of Things will become a reality provided by the proliferation of WSN. For that end, perhaps the most important part of WSN, the backend, needs to be seriously addressed. In the next section, we detail the architecture for our proposed cloud middleware solution that will tackle this issue. . (Wan &Zou, 2013)
Experience on building applications is showing several common properties of wireless sensor networks.
First, with a range of only a few hundred feet at most, sensors often use multi-hop communication; i.e., they relay data through neighboring nodes to the base station.
Second, battery is generally the only source of energy, and it is not feasible to re- place batteries in most sensor deployments. Therefore, it is necessary to minimize energy consumption in order to maximize sensors‘ lifetime.
Third, although communication, processing, and sensing, all consume energy, communication is the single most expensive operation. (Younis et al 2006)
A key shortcoming of current research efforts is a lack of consideration of the WSN backend. Since the nodes of a sensor network have very limited storage and processing capabilities, sensor networks rarely, if ever, operate in isolation and are usually connected to a backend modeling infrastructure. (Lee& Murray, 2010).
CHAPTER THREE
RESEARCH METHODOLOGY
Introduction.
This chapter presents a detailed description of the methodology to be employed in the study. The study proposes to address the vulnerabilities of cloud data by deploying a data encryption model. This model has encryption/decryption service that can be employed either locally or remotely according to level of severity of the data. This model shall remove the burden of key management and maintained from data owners.
Research Design.
According to Donald (2006), a research design is a structure of the research that holds together all the elements of a research project. The study will adopt a quasi-experimental nonrandomized control pretest-posttest quantitative research method is used. It is a quasi-experimental process as the nature of cloud storage means the devices are connected to the Internet to gather data and establish the circumstances for review, which can lead to changes to the devices outside the scope of the experiment. The proposed framework shall ensure that outsourced data can only be accessed (decrypted) by authorized users, and during the whole process cloud server is unable to learn any useful information that can lead to a potential privacy breach. To achieve the privacy of these components, our scheme processes the data in three fundamental steps: data outsourcing, file access and revocation.
Sample population
In quantitative research methodology, a data sample is a setoff data allowed and/ or selected from a statistical population by a defined procedure .Nubisave is freely available space controller 22for RAID or even optimal clouds which make dispersion, this makes data secure beyond encryption and made inaccessible in its entirely to the individual storage providers.
Nubisave was used as the source of data since it freely provides space that securely stores data beyond encryption (Ilaghi Hosseini, (2015).
In this research the population consisted of sixty data storage providers that were extracted from sixty storage systems. The population was then clustered into five cloud storages containing ten cryptographic techniques representing five of the several cloud storages. the five cloud storages used are; Dropbox, SugarSync, Amazon S3, Google storage and T-online media center.
CHAPTER FOUR
EXPERIMENT, RESULTS AND DISCUSSION
Introduction
This chapter discusses the conducted experiment that was involved in designing an effective model that ensures security of data in the cloud. This effective way involved combining AES and Blowfish which increases the run time for both encryption and decryption. This means that the total time required for hybrid algorithms will be the addition of both algorithms‘ run time (processing time).Blowfish requires less time as compared to other algorithms. It also adds the additional processing time thus enhancing the security. This section gives the collected filtered data from the experiment which was then analyzed using graphs and discussed in order to understand the observed outcome.
CHAPTER FIVE:
CONCLUSIONS AND FUTURE WORK
Conclusion
When the clients store data in the cloud, there‘s always an issue whether or not cloud service provider stores the data securely. Security as earlier discussed is the main challenge faced while storing data in the cloud, the proposed system provides security for the data stored in the cloud computing model through the help of AES and Blowfish algorithms.
Results show that AES is the best symmetric encryption algorithm, it‘s more secure than Blowfish though compared to other algorithms Blowfish is by far the best. Blowfish gives the highest throughput as compared to AES. The hybrid of AES and Blowfish gives the properties of both algorithms thus making the formed hybrid algorithm much stronger to threats. This makes the formed hybrid system secure by increasingly adding the complexity functionalities.
Recommendations for Future Work
The future scope of this work can be extended by:
Performing the same experiments using audio and video as well. Compression algorithm can be performed for faster encryption.
Performing the same experiments using some locking techniques for security mechanism
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