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

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@tutorsindia.com

Today's Discussion OUTLINE

In Brief Introduction Why is Cloud Preferred Threats to Cloud Computing Data Security Issues Machine Learning Machine Learning Algorithms Conclusion

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.

Cloud computing technique has been gaining popularity because of its different features such as on demand service and scalability.

Introduction

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.

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.

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.

Data loss Data breach

Threats to Cloud Computing

Malicious insiders Insecure interfaces and APIs Account or service hijacking Data location denial of service

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.

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.

Machine Learning Algorithms Supervised Algorithm A dataset is first created which belongs to different other classes which have a certain identity.

Sensitive Attributes

Sensitive attributes are designated as confidential.

Unsupervised Algorithm The classes employed are not specifically characterized instead information is arranged automatically.

Non-Sensitive Attributes

Non-sensitive attributes is classified as non-confidential.

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.

Noticeably, 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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