How Machine Learning Technique Can Help Cloud Database System- TutorsIndia.com

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HOW MACHINE LEARNING TECHNIQUE CAN HELP CLOUD
DATABASE SYSTEM
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Tutors India
Group www.tutorsindia.com Email
info_at_tutorsindia.com
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Today's Discussion
OUTLINE
In Brief Introduction Why is Cloud Preferred
Threats to Cloud Computing Data Security Issues
Machine Learning Machine Learning Algorithms
Conclusion
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In Brief
Cloud computation has been gaining popularity due
to its application for storing data. Existence
of large volumes of data on cloud has grabbed
the concern of industries towards maintaining
security and privacy of data. In order to secure
the data there have been applied several
approaches such as encryption, cryptography etc.
Recently machine learning algorithms have been
applied to secure the data on cloud.
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Cloud computing technique has been gaining
popularity
because of its different features such as on
demand service and scalability.

• Cloud has been widely employed for storing data
  and other computation intensive applications due
  to which issues such as data security and
  privacy have become of primary concern.

Introduction
While data security is associated with data
confidentiality, availability and integrity, data
privacy is for hiding the identity of data on
cloud. The security issues associated with data
prevail on the cloud until the data is destroyed
or removed from cloud.
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Why is Cloud Preferred?
The users associated with cloud avail different
benefits in terms of accessing the data,
availability and confidentiality of data. In
addition since the data is stored on different
severs on cloud the users are able to access it
from anywhere and anytime without requiring any
physical devices. The data shared over the
network can be accessed by different
clients. Besides, the cloud also offers
facilities such as recoveries from disaster,
archival and backup.
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Data loss
Data breach
Threats to Cloud Computing
Malicious insiders
Insecure interfaces and APIs
Account or service hijacking
Data location denial of service
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Data Security Issues
The data security issues occur primarily because
users and several organizations store their data
on cloud and thus it becomes necessary to prevent
the data from being leaked during its travelling
over the network. There are several methods used
for securing data. These methods are based on
encryption of data and access control. These
methods enable preventing leakage of
data. Cryptographic algorithms have also been
applied to cloud servers to resolve the security
issues.
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Machine Learning

• In machine learning the data classification
  approach is used for differentiating between the
  unclassified and classified data sets.
• The classifier used is build by developing a
  training set of data samples.
• The servers offering such service undertake the
  processing of data and classify the data samples
  of clients.
• Besides classification, it is ensured that
  through this approach the data processed and
  categorized might not be leaked by the servers.

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Machine Learning Algorithms
Unsupervised Algorithm
Supervised Algorithm
A dataset is first created which belongs to
different other classes which have a certain
identity.
The classes employed are not specifically
characterized instead information is arranged
automatically.
Non-Sensitive Attributes
Sensitive Attributes
Non-sensitive attributes is classified as
non-confidential.
Sensitive attributes are designated as
confidential.
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Conclusion
Undeniably, the security and distribution of data
are of paramount significance in the present
world. There has been observed an increase in
the risk of privacy of individuals due to
increase in the attack on data stored when
storing resources like cloud computing are
used. In order to protect the data stored on
cloud it is necessary that different cloud
computing techniques should be employed. Noticeab
ly, with the increase in data storage devices
there has been observed advancements in the
techniques used for maintaining privacy and
security of data.
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