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Big Data Analytics – Integration and Privacy (2018)

Big data is a buzzword which is used to refer to a huge set of data with varied and complex structures correlating to difficulties in the storage of the said data, analysis as well as extracting results. On the other hand, big data analytics is a jargon which means research on big data with the aim of revealing the hidden correlations.

Nonetheless, there is a noticeable inconsistency between the integration and the privacy of big data as well as its widespread use. This dissertation focuses on integration and privacy necessities in big data. The research study covers the integration of big data analytics and also the privacy of big data through employing the existing methods like L-diversity, HybrEx, and T-closeness. For instance, privacy keeping mechanisms have been formulated for the protection of privacy for all stages which are involved the life cycle of big data.

Such stages include the generation of data, storage of data and processing of this data. The main objective of this research study is to review the integration processes of big data analytics and the privacy mechanisms and finally present challenges of using the current privacy preservation mechanisms. This dissertation outlines recent mechanisms of preserving privacy in big data such as identity anonymization, big data streams anonymization as well privacy in big data publishing.

The purpose of this research study is to conduct a survey on how big data is protected or secured at all levels of its life cycle such as security at data generation stage, data storage stage and also data processing phase. The study will use different methodological aspects to investigate and examine big data integrations that are in place as well as evaluate the effectiveness of the said integrations towards the big data privacy and security. This dissertation intends to use the research findings to recommend possible integrations for the effective privacy of big data analytics.


  • 10,000 words – 44 pages in length
  • Excellent use of literature
  • Excellent analysis of subject area
  • Well written throughout
  • Includes questionnaire
  • Ideal for IT students

1 – Introduction
Background Information
Problem Statement
Dissertation Objectives
Conceptual Framework
The Concept of data in the Context of Modern Technology
The Concepts of Integration and Privacy and its value
The Concepts of Integration and Privacy in relation to big data analytics
Dissertation Rationale
Research Hypothesis
Research Questions
Justification of the Study
Ethical Considerations for this Research Study
Outline of the Research

2 – Literature Review
Main Issues in Big Data Privacy and Security
Why Big Data Pose a Threat to Personal Privacy
Connectivity of Social Network
Main Principles of Privacy Protection
Key Technologies of Privacy Protection
Data Warehouse
Data Integration
Big Data Integration
Challenges in Big Data Analytics
Big Data Analytics Integrations and Privacy Preservation Methods

3 – Methodology
Research Design
Population and Sampling
Data Collection Procedures
Data Analysis

4 – Findings
General Data of the Respondents
Items Relating to Use, Integration, and Privacy of Big Data
The effectiveness of the Current Integrations on Big Data Security and Privacy Mechanisms

5 – Discussion

6 – Conclusions and Recommendations
Conclusions
Recommendations
Suggestions for Future Research

7 – Evaluation
Limitations of the Study
Proposed Improvements

References

Appendix

Big Data Analytics Dissertation
Big Data Analytics Dissertation

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