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The 6th Post Graduate Conference of Computer Engineering (cPGCON-2017)
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
Contents
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Introduction and Motivation
Our Contribution
Literature survey
Our Proposed Approach
Methodology of Evaluation
Performance Result Analysis
Conclusions and Future Work
References
cPGCON-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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Introduction..
• Attempts to preserve the privacy has been addressed
in [3-4][10-29][75][79][80-81][172-174][180]
cPGCON-2017, Track=xx, Paper ID=xx,
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Motivation
• We observe in the Anonymization Approaches [3][1029][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-2017, Track=xx, Paper ID=xx,
College code=xx
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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-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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An Illustration
• Give some example, (wherein state How your
proposed approach would solve the problem and
gives the solution in better way?)
cPGCON-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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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
EliminationDispersal
Note: Draw a pictorial representation of your proposed system
cPGCON-2017, Track=xx, Paper ID=xx,
College code=xx
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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-2017, Track=xx, Paper ID=xx,
College code=xx
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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-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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Test Application
• Write the pseudo code of your
proposed algorithm in Courier New
Font of size 22.
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Data Set
• We use Adult dataset from the UCI Machine
Learning Repository with 32561 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-2017, Track=xx, Paper ID=xx,
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Performance Result and Analysis
Figure 1: Information loss for the Adult Dataset
cPGCON-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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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-2017, Track=xx, Paper ID=xx,
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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.
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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.
12-18, 2004.
[2] R. Agrawal and R. Srikant, “Privacy preserving data mining”, In:
ACM SIGMOD Record, Vol. 29, No. 2, pp. 439-450, 2000.
[3] Y. Lindell and B. Pinkas, “Privacy preserving data mining”, In:
Journal of Cryptology, Vol. 15, No. 3, pp. 177-206, 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.
494-513, 2007.
cPGCON-2017, Track=xx, Paper ID=xx,
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Thank You
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