DETECTING DATA LEAKS
Figures and abstract
In today’s world, many businesses must outsource certain business processes (e.g., marketing, human resources) and related activities to a third party, such as their service providers. In many cases, the service provider requires access to the company’s confidential information, such as customer data and bank account information, in order to provide their services.
And the amount of sensitive data used by outsourcing providers continues to rise for most corporations. So, in today’s world, data leakage is one of the most common risks and errors, and preventing data leakage is a business-wide challenge.
As a result, we require a powerful technique capable of detecting such deception. Watermarking has traditionally been used to detect leaks. Watermarks can be extremely useful in some situations, but they do require some modification of the original data.
As a result, in this paper, unobtrusive techniques for detecting leakage of a set of objects or records are investigated. The model was created to assess agents’ “guilt.” The algorithms exist to distribute objects to agents in a way that increases our chances of identifying a leaker. Finally, think about adding “fake” objects to the distributed set.
The main contribution of this system is the development of a guilt model using fictitious data.
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TABLE OF CONTENT
Title page- – – – – – – – – i
Approval page – – – – – – – -ii
Dedication – – – – – – – – -iii
Acknowledgement – – – – – – – -iv
Abstract – – – – – – – – – -v
Table of content – – – – – – – -vi
CHAPTER ONE
INTRODUCTION – – – – – – – -1
1.0 Background of the study – – – – -1
1.1 Statement of the problem – – – – -5
1.2 Purpose of the study – – – – – -6
1.3 Significance of the study – – – – -8
1.4 Research questions – – – – – -9
1.5 Scope of the study – – – – – – -10
CHAPTER TWO
LITERATURE REVIEW – – – – – – -11
CHAPTER THREE
Research methodology – – – – – – -39
Design of study – – – – – – – -40
Area of study – – – – – – – – -40
Population of the study – – – – – – -41
Sample and sampling techniques – – – – -41
Instrument for data collection – – – – -41
Method of data collection – – – – – -42
Method of data analysis – – – – – – -43
CHAPTER FOUR
Presentation, analysis and interpretation of data – -48
Discussion of findings – – – – – – -56
CHAPTER FIVE
Summary of findings – – – – – – -60
Conclusion – – – – – – – – -61
Recommendations – – – – – – – -62
Suggestions for further research – – – – -64
References – – – – – – – – -65
Appendix I – – – – — – – – -68
Questionnaire. – – – – – – – -69
DETECTING DATA LEAKS
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