Design And Implement Experiments To Improve Data Privacy And Security In Cloud Computing

Collecting and Recording Raw Data

The major objective of our paper is to help us to become familiar with the research or experimental processes. We conduct different types of researches or experiments in our lives, and therefore, this paper will be very important us it will enhance our understanding on how we can conduct these researches or experiments effectively (Babbie, 2015). This paper will be based on the previous assignment which discussed privacy and security in cloud computing. As we know, for effective storage, management, and processing of data in cloud computing, the privacy and the security of the data must be highly considered and given the required attention to avoid leakage of the data to the unauthorized persons who can access the data and end up interfering with or messing the entire network system for their personal gains (Hauer, 2015, pp.2554-2565). To achieve our objective in this paper, we shall do a detailed review, conduct an analysis, do a design, and finally implement experiments which will help us to evaluate the methodology in the previous in the previous assignment, and thus report the findings and the results of the previous experiment.

Data sources

There are various sources where we shall collect the data required in our research. These sources will be organizations involved in various operations and use cloud computing in their operations. As we know, cloud computing is a technique which involves using a large network of some remote servers which are normally hosted over the internet for them to store, to manage, and to process data, rather than using the local servers or some personal computers for storing, managing, and processing the data (Rittinghouse and Ransome, 2016). Therefore, the organizations which use cloud computing technology in their operations will be suitable sources of data for our research. In our case, we shall visit three organizations which have cloud computing services in their operations. These organizations will be a systems maintenance organization, a computer producing/selling organization, and a sports organization. All these three organization deal with bulk amounts of data and have cloud computing departments which are concerned with the privacy and the security of their data.

Data collection record

After collecting our desired data in the sources discussed above, we shall record the data in a table which will enhance its presentation and its security (Sun et al., 2014, pp.3601-3604). The table will also give more descriptions of the data which is very important especially to the people who might need to use our report in the future. The data collected will be stored in the sample table shown below:

Converting and Preprocessing Raw Data

Data collection table

Storage of the collected data

After collecting and recording the raw data needed for our research, it’s very important to store it safely for easy access and use in the future (Hsu and Patterson, 2014, 2014). The raw data is converted into the most appropriate format for it to be stored for future use. It’s then stored in a table, and the sample table shown below can be used for storing the collected data.

Data storage table

After collecting any raw data to be used in an experiment or research, it’s good to do some pre-processing of the data, but it can be used in the experiment. In data pre-processing, the collected raw data is modified and transformed into the appropriate formats which are required in the experiment (García, Ramírez-Gallego, Luengo, Benítez, and Herrera, 2016, pp.9-11). There are several common data pre-processing techniques which are used in modifying and transforming the data into the required format. Some of the most common data pre-processing techniques include data cleaning, data integration, data reduction, and data transformation (Zhao, Wang, and Sheng, 2018, pp.13-52). Data cleaning is done to remove noise and inconsistency in the raw data. Data reduction is done to remove redundancy and the unwanted data from the collected raw data. Data integration is done to combine the data from several sources to come up with a coherent and reasonable data which can be used together in the experiment (Gomez-Cabrero et al., 2014). Lastly, data transformation is the last step in data pre-processing and is done to transform the collected data to the final and the most appropriate format which will be used in the experiment or research (Heer, Hellerstein, and Kandel, 2015). Good and effective data pre-processing is very important in experiments or research as it simplifies the process of data analysis.

Feature selection, sometimes referred to as dimension reduction is the process of reducing the inputs to be used in an experimental analysis or finding out the most useful and the most meaningful inputs which should be used in the analysis of an experiment or research (Tang, Alelyani, and Liu, 2014, pp.37-39). Feature selection helps in reducing the dimensionality of the collected data which simplifies the process of data analysis which comes later (Hira and Gillies, 2015). Therefore, we can say that data pre-processing and feature selection serve the same role as they help in modifying the data which simplifies the process of data analysis which comes at later stages. After doing dimension reduction or feature selection, the data obtained is normally recorded in another table, and at this time, this data is usually ready and can now be used in the experiment. The sample table shown below can be used in recording or tabulating our data after the process of feature selection or dimension reduction:

Feature Selection

Feature selection table

We have various types of methodologies used in research designs. The most common types of methodologies are qualitative and quantitative methodologies (O’Brien and Saldanha, 2014). Qualitative research methodology is the methodology which deals with collection and analysis of non-numerical forms of data (Silverman, 2016). Quantitative methodology, on the other hand, is the type of methodology which deals with the collection and analysis of numerical data (Vickers, Larsen, Knapp, Kabrick, and Dey, 2017, pp.1677-1686). In our previous assignment, we chose qualitative methodology as our main research methodology which we used in the entire assignment. In this paper, we shall still use qualitative methodology in our design where we’ll give a summary of all the major steps which we’ll use to make the research successful. These steps can be summarized by the sample table shown below:

Experiment design table

To effectively implement our research or experiment, we used the appropriate software and tools in the analysis of the collected information. Some tables and graphs were used to tabulate the results obtained after analyzing the collected data (Chambers, 2017). In this case, the research was dealing with the analysis of the cloud computing techniques used to enhance the privacy and the security of the data in our three organizations of our concern. To implement our research, we first analyzed and sorted the major privacy and security issues which were affecting the cloud computing techniques used by the organizations. We found that the main cloud computing used by these organizations were highly affected by many security challenges where the main challenges which affected these techniques included data breaches, account hijacking, malware addition, and internal threats. After analyzing the major challenges which affected the cloud computing techniques used in the organizations, we were able to make good modifications to the existing cloud computing techniques and came up with some other better techniques which helped to address the major challenges which faced the previous techniques. So, we can say that our research or experiment implementation was successful since we were able to modify the existing cloud computing techniques and came up with other better techniques which helped to solve many of the security challenges which faced the previous cloud computing techniques used in the organizations.

Our experiment was aimed at improving the privacy and the security of the private data of the organizations of our research. All the organizations should always be very careful about the security of their data. Many organizations have ended up in great losses due to leakage of their private data to the unauthorized malicious people who end up harming the organizations for their own gains. To avoid experiencing such cases, organizations must make sure they are equipped with the best and the strongest data protection techniques which won’t in any way allow the private data of the organizations to leak to the unauthorized people operating in or out of the organizations (Mehmood, Natgunanathan, Xiang, Hua, and Guo, 2016, pp.1821-1834). When doing our research, we aimed at improving the privacy and the security of the organizations’ data by coming up with modified and other better cloud computing and protection techniques which would help to enhance the security of the data of these organizations. The results expected from the research were improved security measures which could mean that the organizations would have better control and management of their data which could improve their overall performance. The performance of organizations is usually dependent on how the organizations control and manage their data to harness the best from the data.

As already stated above, the results expected from our research were improved data security measures which could help the organizations to have good control and management of their data. Our research question sought to address how the security of organizations’ data can be improved by using cloud computing techniques and other useful data protection schemes. The results expected in our research are relevant and helps to address the research question effectively as they aim to improve the security of the organizations’ data which will consequently improve the overall performance of organizations as discussed above.

References

Babbie, E.R., 2015. The practice of social research. Nelson Education.

Chambers, J.M., 2017. Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC.

García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J.M. and Herrera, F., 2016. Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), pp.9-11.

Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A. and Tegnér, J., 2014. Data integration in the era of omics: current and future challenges.

Hauer, B., 2015. Data and information leakage prevention within the scope of information security. IEEE Access, 3 (1), pp.2554-2565.

Heer, J., Hellerstein, J.M. and Kandel, S., 2015. Predictive Interaction for Data Transformation. In CIDR.

Hira, Z.M. and Gillies, D.F., 2015. A review of feature selection and feature extraction methods applied on microarray data. Advances in bioinformatics, 2015.

Hsu, W.W. and Patterson, R.H., 2014. System and method for providing long-term storage for data. U.S. Patent 8,635,184.

Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G. and Guo, S., 2016. Protection of big data privacy. IEEE access, 4, pp.1821-1834.

O’Brien, S. and Saldanha, G., 2014. Research methodologies in translation studies. Routledge.

Rittinghouse, J.W. and Ransome, J.F., 2016. Cloud computing: implementation, management, and security. CRC press.

Silverman, D. ed., 2016. Qualitative research. Sage Publications.

Sun, H., Liu, S., Lin, W., Zhang, K.Y., Lv, W., Huang, X., Huo, F., Yang, H., Jenkins, G., Zhao, Q. and Huang, W., 2014. Smart responsive phosphorescent materials for data recording and security protection. Nature communications, 5 (2), pp.3601-3604.

Tang, J., Alelyani, S. and Liu, H., 2014. Feature selection for classification: A review. Data classification: Algorithms and applications, pp.37-39.

Vickers, L.A., Larsen, D.R., Knapp, B.O., Kabrick, J.M. and Dey, D.C., 2017. Reference charts for young stands—a quantitative methodology for assessing tree performance. Canadian Journal of Forest Research, 47(12), pp.1677-1686.

Zhao, J., Wang, W. and Sheng, C., 2018. Data Preprocessing Techniques. In Data-Driven Prediction for Industrial Processes and Their Applications (pp. 13-52). Springer, Cham.

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