1. SECURE CLOUD MINING
Internal Guide Presented By
Prof. K.K. Devadkar Ahlam Shakeel A. Ansari

2. Outline
• Abstract
• Introduction
• Cloud Storage
• Data Mining
• Proposed Architecture
• Conclusion
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3. ABSTRACT
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4. Cloud computing provides cheap and
efficient solutions of storing and analyzing
mass data and it aims to power up the next
generation data centers. Many companies
are opting for cloud storage and shifting
their data ware house over cloud; hence it is
important to use an efficient and effective
data mining strategy to mine the cloud
storage to extract interesting patterns and
relationship between variables in large
databases.
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5. These data patterns are nothing but
forecasting or predictions that can be used
by the companies in near future to increase
their sales, develop its new marketing
strategy. But if the cloud used is public cloud
then the predictions that are generated as a
result of mining should be secure from
attacks such as interception. Hence we
propose a Secure Cloud Mining (SCM)
architecture that will generate a Secure
Forecasting Report (SFR) for the companies
that will use cloud mining.
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6. INTRODUCTION
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7. Publish
Inform scale
Interact
web
Integrate
Transact
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Discover (intelligence)
Semantic
discovery
Automate (discovery)
HPC, cloud
Data-intensive
Social media and networking
Data marketplace and analytics
time
7
deep web
Evolution of Internet Computing

8. Evolution of Computing
Mainframe
Personal
Computer
Client - Server
Web Services
Cloud
Computing
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9. • Cloud distributes the computing tasks to the
resource pool constituted of a large number
of computers, so that a variety of application
systems can obtain computing power,
storage space and a variety of software
services on demand.
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10. • A public cloud is one based on the standard cloud computing
model, in which a service provider makes resources, such as
applications and storage, available to the general public over
the Internet. Public cloud services may be free or offered on
a pay-per-usage model.
• The main benefits of using a public cloud service are:
– Easy and inexpensive set-up because hardware, application and
bandwidth costs are covered by the provider.
– Scalability to meet needs.
– No wasted resources because you pay for what you use.
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11. • We use the distributed association rule mining algorithm for
generating the prediction reports.
• In this seminar an architecture is proposed that can be used
by the companies for prediction analytics so they can device
some marketing strategies accordingly by mining their data
over the cloud and that provides them with a secure
forecasting report.
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12. CLOUD
STORAGE
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13. MOBILE
PC
REMOTE DESKTOP
Cloud
Storage
DATABASE
REMOTE SERVER
NOTEBOOK TABLET PC
FIG: Overview of Cloud Storage
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14. DATA MINING
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15. Calculate Calculate
Confidence & broad Confidence & broad
cast it to other nodes cast it to other nodes
Accept the global Accept the global
confidence and confidence and
generate item set generate item set
Storage Storage Storage
Node Node Node
FIG: Improved Distributed Apriori
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16. TIME (MS)
SUPPORT FACTOR
Graph: Performance of Distributed Apriori and Apriori
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17. PROPOSED
SOLUTION
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18. 5
Input Dataset Worker Servers
6
Decryption Encryption 4
Website Interface
7
Crypto-Unit
3
2
Task Queue
Task Log
8
Storage
1
Browser
18
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19. Forecasting
Report
Select the Image that
Encrypt the Report with
user has selected as a
the key
key
Secure Forecasting
Report
Encryption
FIG: Encryption Unit
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20. Encrypted
Forecasting
Report Request
Decrypt the Report
Select the Image key
Request using the key
Report Request
Decryption
FIG: Decryption Unit
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21. Example
• Let’s take an example to understand the architecture in a
better way. There is a super market that has wide range of
products and it keeps track of all it customers and what
things they have shopped and accordingly the super mart
provides them with offers.
• Now the super mart marketing manager wants to know how
many products are sold in association i.e. if a customer
purchases a book he is likely to purchase a pen or pencil
with it, and if he/she purchase a pencil the its likely that
he/she will purchase an eraser or sharpener with it, so that
the marketing manager can devise a marketing strategy
accordingly to lure the customer and increase its sales.
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22. CONCLUSION
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23. By use of cloud computing we are
provided with cheap and efficient solutions
of storing and analyzing mass data. The
companies that opt for public cloud storage
and secure cloud mining can increase their
sales and develop new marketing strategy by
simply using predictive analysis as the cost
incurred in the set up is also low as compared
to setting up a data ware house for the
company.
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26. REFERENCE
1. A Weiss. “Computing in Clouds”, ACM Networker, 11(4):18-
25, Dec.2007.
2. R Buyya, CS Yeo, S Venugopal, Market-Oriented Cloud
Computing:Vision, Hype, and Reality for Delivering IT
Services as Computing Utilities. Proceedings of the 2008
10th IEEE International Conference on High Performance
Computing and Communications, Vol.00, pp, 5-13, 2008.
3. Storage Networking Industry Association and the Open Grid
Forum, Cloud Storage for Cloud Computing, pp. 4-7, Storage
Networking Industry Association (September 2009).
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27. 4. U.Fayyad, G. Piatetsky-Shapiro, and P.Smyth, “From data
mining to knowledge discovery in databases”, AI Magazine,
Vol. 17, 1996, pp.37-54.
5. Huimin Wang , Guihua Nie, Kui Fu, “Distributed data mining
based on semantic web and grid”, 2009 International
Conference on Computational Intelligence and Natural
Computing.
6. Lingjuan Li, Min Zhang, “The Strategy of Mining Association
Rule Based on Cloud Computing”, 2011 International
Conference on Business Computing and Global
Informatization.
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28. 7. Shao Feng jing, Yu Zhong qing. Principle and
Algorithm of Data Mining [M]. Beijing: China
WaterPower Press, 2003. 2, 126-170.
8. Ansari Ahlam, “Cloud Computing Security using
Hybrid Distributed Intrusion Detection System”,
International Journal of Advances in Management,
Technology & Engineering Sciences, 2012
9. http://www.focus.com/fyi/operations/10-largest-
databases-in-the-world/
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