Authors :
Rubia Fathima
Volume/Issue :
Volume 6 - 2021, Issue 8 - August
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3AZxtk5
Abstract :
:- The Data reflects Humans as most of the data is
online. Privacy for individual's data has become a prime
concern. Securing and preserving that privacy has been a
focus for long decades. In data analytics, machine learning
and data science analysis over users' private data utilize to
understand the individual user's responses to present data
publicly. The issue with private data presented online that
consisted of personal private data was sensitive and
confidential was a significant issue, so a particular group
of mathematicians and cryptographers came together to
resolve this issue by introducing the concept of Differential
Privacy.
Differential privacy is assurance over information privacy
without damaging the chance of having privacy risk by
including some amount of Random noise in the form of
robust data to the original dataset. Differential privacy is
also a tool with an algorithm that helps maintain Privacy
by Preserving and Randomizing data responses—
measuring the accuracy of statistical data by performing
analysis. To Perform this process of differential privacy,
IBM developed an open-sourced algorithm called
Diffprivlib[1]. With this library, the project has created a
Front-End Web application that can perform data analysis
that involves different mechanisms, models, and Tools.
This project is an attempt to integrate all mechanisms,
models, and tools involved in DiffPrivLib[1]. The primary
purpose of this paper is to showcase the work on
differential privacy that consists in developing a userfriendly web application that can be open-sourced. This
application is designed in a python programming package
and will experiment with the dataset to perform the
analysis to show the impact of differential privacy
algorithms on different values on epsilon with accuracy
and privacy.
Keywords :
Differential Privacy, Python Programming, OpenSource Library, Data Science, Machine Learning, Data Analytics.
:- The Data reflects Humans as most of the data is
online. Privacy for individual's data has become a prime
concern. Securing and preserving that privacy has been a
focus for long decades. In data analytics, machine learning
and data science analysis over users' private data utilize to
understand the individual user's responses to present data
publicly. The issue with private data presented online that
consisted of personal private data was sensitive and
confidential was a significant issue, so a particular group
of mathematicians and cryptographers came together to
resolve this issue by introducing the concept of Differential
Privacy.
Differential privacy is assurance over information privacy
without damaging the chance of having privacy risk by
including some amount of Random noise in the form of
robust data to the original dataset. Differential privacy is
also a tool with an algorithm that helps maintain Privacy
by Preserving and Randomizing data responses—
measuring the accuracy of statistical data by performing
analysis. To Perform this process of differential privacy,
IBM developed an open-sourced algorithm called
Diffprivlib[1]. With this library, the project has created a
Front-End Web application that can perform data analysis
that involves different mechanisms, models, and Tools.
This project is an attempt to integrate all mechanisms,
models, and tools involved in DiffPrivLib[1]. The primary
purpose of this paper is to showcase the work on
differential privacy that consists in developing a userfriendly web application that can be open-sourced. This
application is designed in a python programming package
and will experiment with the dataset to perform the
analysis to show the impact of differential privacy
algorithms on different values on epsilon with accuracy
and privacy.
Keywords :
Differential Privacy, Python Programming, OpenSource Library, Data Science, Machine Learning, Data Analytics.
