1. Privacy Preserving Data Publishing
The 7th Post Graduate Conference of Computer Engineering (cPGCON-2018)
Privacy Preserving Data Publishing
Paper ID: XX
Track: Wireless Networks and Communications
Presented by: Mr/Ms XYZ
Guided By: Prof/Dr XYZ
College Name: XYZ
College Code: XX

2. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Contents
Introduction and Motivation
Our Contribution
Literature survey
Our Proposed Approach
Methodology of Evaluation
Performance Result Analysis
Conclusions and Future Work
References
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

3. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Introduction
A large amount of data has been collected by various organization viz. Medical and Insurance
For the Research and analysis
Contains sensitive personal information
Privacy related incidents occur in [1-7] [9-40] [50-64]
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

4. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Introduction..
Attempts to preserve the privacy has been addressed in [3-4][10-29][75][79][80-81][ ][180]
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

5. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Motivation
We observe in the Anonymization Approaches [3][10-29][49][74-77][79][80-81] that
there is tradeoff between privacy and information loss
We notice that the k-anonymity model
Suffer from the information loss due to generalization and suppression
Could not maintain the diversity among the sensitive attribute
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

6. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Our Contribution
We propose a sensitive attribute based clustering approach for the k-anonymity model.
For minimizing the information loss
For minimizing the disclosure risk
To maintain the diversity among the sensitive attributes
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

7. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Literature Survey
Various approaches [1-20][30-50][53-77] have been proposed in the literature for PPDM.
Traditional Approaches [68-73][75]
Disclose the data using inferences from original data
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

8. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
An Illustration
Give some example, (wherein state How your proposed approach would solve the problem and gives the solution in better way?)
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

9. Our Proposed Approach/Mathematical Model
The central outline of the proposed algorithm is as follows.
Step 1: We first load the database.
Step 2: We identify and classify the attributes such as identifier, quasi-identifier and sensitive attribute in a database.
Step 3:…
Step 4:…
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

10. Our Proposed Approach ..
Database
Initial Solution Encoding-Grouping
Solution Encoding
Distance Matrix
Grouping with the k and l parameters
Objective Function
Bacterial Foraging Optimization
Chemotaxis
Reproduction
Elimination- Dispersal
Note: Draw a pictorial representation of your proposed system
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

11. Methodology of Evaluation
We compare our proposed approach with state of the art clustering approaches viz.
Kabir et al. [17] Systematic clustering algorithm, 2011
Byun et al. [12] Greedy k-member algorithm, 2007
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

12. Methodology of Evaluation..
We use Visual Basic 6.0 and Microsoft Access 2007 for the implementation and run on 3.2 GHz Intel Core 2 Duo Processor machine with 2 GB RAM.
The Microsoft Windows XP Professional is used as an operating system.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

13. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Metrics of Evaluation
We evaluated our proposed approach with respect to the parameters
such as information loss and execution time.
We ran our proposed approach on the various
k-values such as 20, 40, 60, 80 and 100.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

14. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Test Application
Write the pseudo code of your proposed algorithm in Courier New Font of size 22.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

15. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Data Set
We use Adult dataset from the UCI Machine Learning Repository with records and 14 attributes.
Out of them, we retain only attributes viz. Age, Race, Marital-status, Sex, fnlwt and Occupation.
The attribute Occupation is taken as a sensitive attribute in the dataset.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

16. Performance Result and Analysis
Figure 1: Information loss for the Adult Dataset
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

17. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Our Observations
It is indeed feasible to make a cluster based on sensitive attribute for minimizing the disclosure risk with lesser information loss.
During the evaluation, we notice that our proposed approach,
Sometime our algorithm is affected with similar sensitive attribute, if the real dataset contain similar kind of sensitive attribute.
Thus, it becomes simpler to the miner to identify an individuals.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

18. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Conclusion
In our Approach, we proposed a sensitive attribute for the k-anonymity model.
The empirical evaluations shows that it is feasible to achieve lesser information loss at k≦40 instead of setting higher value of k.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

19. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Future Work
In our research attempt,
we have focused on the clustering of the static and centralized database in privacy preserving data mining.
However, the database is growing tremendously via use of the Internet.
Thus, our future work would be to extend the k-anonymity and the l-diversity model using the BFO algorithm to the dynamic and distributed database.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

20. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
References
[1] A. I. Anton, Q. He and D. L. Baumer, “Inside JetBlue’s privacy policy violations”, In: IEEE Security and Privacy, Vol. 2, No. 6, pp , 2004.
[2] R. Agrawal and R. Srikant, “Privacy preserving data mining”, In: ACM SIGMOD Record, Vol. 29, No. 2, pp , 2000.
[3] Y. Lindell and B. Pinkas, “Privacy preserving data mining”, In: Journal of Cryptology, Vol. 15, No. 3, pp , 2002.
[4] F. D. Schoeman, “Philosophical dimensions of privacy: an anthology”, In: Cambridge University Press, 1984.
[5] G. J. Walters, “Human Rights in an information age: a philosophical analysis”, In: Chapter 5, University of Toronto Press, 2002.
[6] J. Zhan, “Using cryptography for privacy protection in data mining system”, In: Proceedings of the 1st WICI International Workshop on Web Intelligence Meets Brain Informatics (WImBI), LNCS 4845, pp , 2007.
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx

21. cPGCON-2018, Track=xx, Paper ID=xx, College code=xx
Thank You
cPGCON-2018, Track=xx, Paper ID=xx, College code=xx