I D S Deva

Ii C Saravanan

Iii C Kanimozhi

Kiruba Dafi

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 ISSN : 2347 - 8446 (Online)
 ISSN : 2347 - 9817 (Print)
www.ijarcst.com
International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2014)
© All Rights Reserved, IJARCST 201467
Vol. 2   Issue 1 Jan-March 2014 
Two-Phase Top-down Specialization for High Scalability and 
Privacy Concerns
ID.S Deva Kiruba Dafi, IIC.Saravanan, IIIC.Kanimozhi
I,II,IIIVelTech MultiTech DR.Rangarajan & DR.Sakunthala Engineering College, Chennai, India
I. Introduction
Privacy is one of the most concerned issues in cloud computing. 
Personal data like financial transaction records and electronic 
health records are extremely sensitive although that can be 
analyzed and mined by organization. Data privacy issues need 
to be addressed urgently before data sets are shared on cloud. Data 
anonymization refers to as hiding sensitive data for owners of data 
records. Large-scale data sets are generalized using two phase 
top-down specialization for data anonymization. This process 
split into two phases. At first mapreduce is applied to the top-
down specialization (TDS) and deliberately design a group of 
innovative mapreduce jobs to accomplish the specialization in 
high scalable fashion. In second one againthe two-phase top-down 
specialization is applied to multiple data partition to improve 
scalability and privacy.
II. Generalization Approach
Data set D contain r number of data records. Each data records have 
m number of attributes. This attributes are arranged in taxonomy 
tree structure. The attribute in the taxonomy tree is denoted as 
TT. Quasi-identifiers QID representing group of anonymous 
records. 
Fig. 1: Taxonomy Tree Structure of Attributes
The top most value of the tree is T. Initially, Cuti contains only 
the top most value for its attribute. The beneficial specialization 
in UCuti from the data set to be performed next. At each iteration, 
find the highest score, denote Best apply to T and update UCuti 
and update score and validity of the attribute. When Top-Down 
specialization is applied to the taxonomy tree structure first it Find 
the best specialization, then perform specialization again and finally 
update values for the next round. Values for the each specialization 
are analyzed. The highest IGPL value for specialization is regard 
as the best specialization. In specialization the data sets are split 
into two phases. The values are updated until k-anonymity.
III. Large-Scale Data Processing Framework
To address the scalability problem of the Top-Down Specialization 
(TDS) approach for large scale data set used a widely adopted 
parallel data processing framework like MapReduce.Mapreduce 
must have two phases. In first phase, the original datasets are 
partitioned into group of smaller datasets and these datasets are 
anonymized in parallel producing intermediate results. In second 
phase, these intermediate results are integrated into one and further 
anonymized to achieve consistent k-anonymous dataset.
Fig. 2: MapReduce Dataflow
Mapreduce used to splitting up the large input data into chunks of 
more or equal size, spinning up a number of processing instances 
for the map phase apportioning data to each of the mappers, tracking 
the status of each mapper, routing the map results to the reduce 
phase and finally shutting down the mappers and the reducers 
when the work has been done. It is easy to scale up MapReduce 
to handle bigger jobs or to produce results in a shorter time by 
simply running the job on a larger cluster. When Mapreduce is 
not used the process fails in distribution system.
Abstract
Sharing the private data like financial transaction record in its most specific state poses a threat to individual privacy.MapReduce 
algorithm for determining generalization and provide protection for sensitive information. Data sets are generalized in a top-down 
manner until k-anonymity is violated, in order to expose the maximum utility. This Top-Down Specialization is efficient for high 
scalability and privacy concerns. High scalable two-phase top-down approach to anonymize large-scale data using mapreduce is 
proposed.Experimental evaluation result shows that security and privacy preservation of top-down specialization can be significantly 
improved over existing approach.
Keywords
MapReduce, k-anonymity, Sensitive Information, Data Sets
 ISSN : 2347 - 8446 (Online)
 ISSN : 2347 - 9817 (Print)
www.ijarcst.com
International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2014)
© 2014, IJARCST All Rights Reserved 68
Vol. 2   Issue 1 Jan-March 2014 
IV. Experimental Process
Three groups of experimental in this section to evaluate the 
effectiveness and efficiency of the approach. In the first one, 
compared TPTDS with CentTDS from the perspectives of 
scalability and efficiency. In the other two, investigate on the trade-
off between scalability and data utility via adjusting configurations. 
Generally, the execution time and ILoss are affected by three 
factors, namely, the size of a data set (S), the number of data 
partitions (p) and the intermediate anonymity parameter (kI). How 
the three factors influence the execution time and ILoss of TPTDS 
is observed. In the first group, measured the change of execution 
time TCent and TTP with respect to S when p=1. The size S varies 
from 50 MB to 2.5 GB. In the second group, p is set as 3. The value 
of p (p>1) is selected randomly and does not affect our analysis 
as what we want to see is the trend of TTP and ILTP with respect 
to kI. In the third group, kI is set as 50,000. The value is selected 
randomly and does not affect our analysis because what we want 
to see is the trend of TTP and ILTP with respect to the number 
partitions. The number of partitions varies from 1 to 20.
 
                     (a)                (b)
Fig. 3: Change in Execution Time and ILoss w.r.t Number of 
Partitions
V. Conclusion
In this paper, a highly scalable two-phase TDS approach is 
proposed using MapReduce on cloud. Data sets are partitioned and 
anonymized in parallel in the first phase, producing intermediate 
results. Then, the intermediate results are merged and further 
anonymized to produce consistent k-anonymous data sets in the 
second phase. Experimental results on real-world datasets have 
demonstrated that with our approach, the scalability and privacy 
of TDS are improved significantly over existing approach.
References
[1] D. Zissis and D. Lekkas, “Addressing Cloud Computing 
Security Issues,” Fut. Gener. Comput Syst., vol. 28, no. 3, 
pp 583-592, 2011. 
[2] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. 
Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica and 
M. Zaharia, “A View of Cloud Computing,” Commun. ACM, 
vol. 53, no. 4, pp. 50-58, 2010.
[3] X. Zhang, Chang Liu, S. Nepal, S. Pandey and J. Chen, “A 
Privacy Leakage Upper-Bound Constraint Based Approach 
for Coat-Effective Privacy Preserving of Intermediate 
Datasets in Cloud,” IEEE Trans. Parallel Distrib, Syst., In 
Press, 2012.
[4] B. Fung, K. Wang, L. Wang and P.C.K. Hung, “Privacy-
Preserving Data Publishing for Cluster Analysis,” Data 
Knowl. Eng., vol. 68, no. 6, pp. 552-575, 2009.
C.BALA SARAVANAN received the 
M.Tech (IT) from Sathyabama University 
in 2011. During 2009-2010, I stayed in 
orbit technologies as software engineer to 
develop health care automation tool. And I 
am doing (Ph.D) in VELTECH University 
and I am now working in VelTech 
MultiTech Dr.RR & Dr.SR Engineering 
College as Assistant Professor and 
IBM TGMC Project coordinator and I 
published more than 20 journals in varies 
journal section.
DEVA KIRUBA DAFI D.S pursuing Final 
Year M.Tech (IT) from VelTech MultiTech 
Dr.RR & Dr.SR Engineering College.
C.KANIMOZHI pursuing final Year 
M.Tech (IT) from VelTech MultiTech 
Dr.RR & Dr.SR Engineering College.


Two-Phase Top-down Specialization for High Scalability and Privacy Concerns

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Two-Phase Top-down Specialization for High Scalability and Privacy Concerns

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