SECURE MINING OF RULES IN HORIZONTLLY DISTRIBUTED DATABASE

1. SECURE MINING OF RULES
IN HORIZONTLLY
DISTRIBUTED DATABASE
BY Mr. BAMANE VINAY VISHNU
UNDER THE GUIDANCE OF Prof. VIPUL BAG

2. Outline
 Introduction
 Literature Survey
 Problem Statement
 Necessity of Proposed System
 Proposed System
 Detail Design
 Implementation
 Conclusion and Future Scope
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3. Introduction
Secure mining of association rules in Horizontally Distributed databases
 Fast Distributed Mining (FDM) algorithm unsecured distributed version of the Apriori
algorithm.
 Two novel secure multi-party algorithms
1. The union of private subsets that each of the interacting players hold.
2. The inclusion of an element held by one player in a subset held by another.
 Simpler and is significantly more efficient in terms of communication rounds,
communication cost and computational costs.
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4. Literature Survey
 Secure Mining of Association Rules in Horizontally Distribute Databases.
 Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned
Data
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1. Databases that share the same schema but hold information on different entities.
2. Distributed association rule mining techniques can discover association rules
among multiple sites.
3. Each site faced problem of communication and computation overhead.
4. When computing global frequent item sets as number of sites increases overhead
also increases.
Secure Mining of Association Rules in Horizontally
Distribute Databases

6. Privacy-Preserving Distributed Mining of Association
Rules on Horizontally Partitioned Data
 Encryption technology to minimize the sharing of information.
 Privacy concerns may prevent the parties from directly sharing the data.
 Secure multi party computation protocol, holds frequent item sets globally.
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7. Problem Statement
 It is a difficult task to securely mining association rules from horizontal distributed
database.
 In case need of efficient algorithms for mining frequent item sets are crucial for
mining association rules.
 The challenge found in frequent item sets mining is a large number of result
patterns are generated.
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8. Necessity of Proposed System
 There is a need to optimize the process of finding patterns.
• Which should detect the important patterns and generate association rules in distributed
database.
 There is also need of a preserving privacy of transaction database
• Because data processed among databases in some business environments.
 Distributed among several sites, but none of the sites is allowed to expose its database
to another.
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9. Proposed System
 The application is designed in such a way which secure mining of association rules
in distributed database and also protecting the data records.
 The application monitors during the time number of site should be able to learn
contents of transactions at any other site and also maintain security and data
efficiency.
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10. Modules Design
 User Module.
 Admin Module.
 Privacy Preserving Data Mining.
 Distributed Computation.
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11. User Module
 Privacy preserving data mining has considered two related settings
• The data owner and the data miner are two different entities.
• Data is distributed among several parties who aim to jointly perform data mining.
 In the first setting, the goal is to protect the data records from the data miner.
• General trends in the data, without revealing original record information.
 In the second setting the goal is to perform data mining
• While protecting the data records of each of the data owners from the other data owners.
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12. User Module
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 Home
 Fund Transfer
 Add Third Party
 Transitions

13. User Module
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 Home
 Fund Transfer
 Add Third Party
 Transitions

14. User Module
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 Home
 Fund Transfer
 Add Third Party
 Transitions

15. User Module
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 Home
 Fund Transfer
 Add Third Party
 Transitions

16. Admin Module
 View user details
 The item set based on the user processing details using association role
with Apriori algorithm.
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17. Admin Module
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 Home
 Register
 Amount
 Verify Third party
 Transitions

18. Admin Module
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 Home
 Register
 Amount
 Verify Third party
 Transitions

19. Admin SBI
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 Home
 Register
 Amount
 Verify Third party
 Transitions

20. Admin IDBI
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 Home
 Register
 Amount
 Verify Third party
 Transitions

21. Admin HDFC
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 Home
 Register
 Amount
 Verify Third party
 Transitions

22. Privacy Preserving Data Mining
 The data owner and the data miner are two
different entities
• To protect the data records from the data
miner.
• The data owner aims at anonym zing the data
prior to its release.
• Perform data mining while protecting the data
records of each of the data owners from the
other data owners.
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23. Privacy Preserving Data Mining
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24. Distributed Computation
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 The two implementations with respect to
three measures:
1) Total computation time of the complete
protocols . The Apriori computation time, and
the time to identify the globally s-frequent item
sets.
2) Total computation time of the unification
protocols all players.
3) Total message size. • N — the number of
transactions in the unified database.

25. Detail Design
 Activity Diagram
 Sequence Diagram
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26. Activity Diagram
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27. Sequence Diagram
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Fig. Sequence Diagram Admin

28. Sequence Diagram
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Fig. Sequence Diagram User

29. Conclusion and Future Work
 As the algorithm generate strong association rules from different data sets
spread over various sites and also preserving privacy of data.
 This application can be used for Business Analysis in Banking Sector.
 The research can be also extended association rules from different area
like stock market, college, school and medical datasets.
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30. Publications
 Secure Extraction Of Association Rules In Parallel Disseminated Data
Paper ID – IJCSN-2016-5-5-111
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