International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
Misusability Weight Measure Using Ensemble
Approaches
Sridevi Sakhamuri1
1
V.Ramachandran 2
Dr.Rupa3
PG Student (M.Tech CSE), Vasireddy Venkatadri institute of Technology, Guntur, AP, India
2
Research Scholor, Acharya Nagarjuna University, Nambur, Guntur, AP, India
3
Professor & H.O.D, Vasireddy Venkatadri institute of Technology, Guntur, AP, India
ABSTRACT
Assigning a misusability weight to a given dataset is strongly related to the way the data is presented (e.g.,
tabular data, structured or free text) and is domain specific. Therefore, one measure of misusability weight cannot fit
all types of data in every domain but it gives a fair idea on how to proceed to handle sensitive data. Previous
approaches such as M-score models that consider number of entities, anonymity levels, number of properties and
values of properties to estimate misusability value for a data record has better efficiency in deducting record
sensitivities. Quality of data, Quantity data, and the distinguishing attributes are vital factors that can influence Mscore. Combined with record ranking and knowledge models prior Approaches used one domain expert for
deducting sensitive information. But for better performance and accuracy we propose to use the effect of combining
knowledge from several experts (e.g., ensemble of knowledge models). Also we plan to extend the computations of
sensitivity level of sensitive attributes to be objectively obtained by using machine learning techniques such as SVM
classifier along with expert scoring models. This approach particularly fits the sensitive parameter values to the
customer value based on customer activity which is far more efficient compared to face value specification with
human involvement. A practical implementation of the proposed system validates our claim.
INTRODUCTION:
Data represent today an important asset for
companies and organizations. In the any organization
the data may cost the worth of million and for that we
want to take the care of the data, the control access
must be handled care to the data with respect to both
internal users, and external users, within/outside of
the organization. Security issues are becoming
increasingly crucial, especially with regard to
ensuring the protection of data from “fabrication,
modification, and interruption”. To assure the
security and privacy of data assets is a crucial and
very difficult problem in our modern networked
world. Privacy preservation has become a major issue
in many data mining applications. Ensuring the
security and privacy of data assets is a crucial and
very difficult problem in our modern networked
world. Overall, data security has a central role in the
ISSN: 2231-5381
larger context of information systems security. The
development of Database Management Systems
(DBMS) with high-assurance security is a central
research issue, It requires a revision of architectures
and techniques adopted by traditional DBMS.
Relational database management systems (RDBMS)
are the fundamental means of data organization,
storage and access in most organizations, services,
and applications. A data set is released to other
parties for data mining; some privacy-preserving
technique is often required to reduce the possibility
of identifying sensitive information about
individuals. Most statistical solutions concern more
about maintaining statistical invariant of data.
The ubiquity of RDBMSs led to the prevalence of
security threats against the systems. Consider, an
intruder from the outside may be able to gain
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
unauthorized access to data by sending carefully
crafted queries to a back-end database of a Web
application. The data mining community has been
studied at building strong privacy-preserving models
and designing efficient optimal and scalable heuristic
solutions. An insider attack against an RDBMS is
much more difficult to detect, and potentially much
more dangerous. The constraints and dependencies
that exist in the data model are used to generate the
dependency Matrix, which in turn will be used to
construct the knowledge graph of the insider.
The goal of the work reported in our scheme
is focus on a study on the k-anonymity property .The
quasi-identifier is the related model for k-anonymity
which is a set of attributes that may serve as an
identifier in the data set. Let Q be the quasi-identifier.
An equivalence class of a table with respect to Q is a
collection of all tuples in the table containing
identical values for Q. The size of an equivalence
class indicates the strength of identification
protection of individuals in the equivalent class. It
would be difficult to re-identify the tuples
individually when the no. of tuples in the equivalence
class is more. A data set D is k-anonymous with
respect to Q if the size of every equivalence class
with respect to Q is k or more. Consider the example
raw medical data set in table 1 having the attributes
job, birth, postcode, illness.
JOB BIRTH POSTCODE ILLNESS
Cat1
1975
4350
HIV
Cat1
1955
4350
HIV
Cat1
1955
5432
Flu
Cat2
1955
5432
Fever
Cat2
1975
4350
Flu
Cat2
1975
4350
fever
TABLE 1: RAW MEDICAL DATA SET
There may be identified that the two records are reidentified as the unique because of having the same
Job, Post code and the Illness where as the birth has
been differ. The table is generalized as a 2anonymous table as in Table 2.
JOB
Cat1
BIRTH
*
POSTCODE
4350
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ILLNESS
HIV
Cat1
*
4350
HIV
Cat1
1955
5432
Flu
Cat2
1955
5432
Fever
Cat2
1975
4350
Flu
Cat2
1975
4350
fever
TABLE 2: A 2-ANONYMOUS DATA SET
In our survey we have noticed that k-anonymization
has 2 main models. They are
1. Global recording
2. Local recording
All values of an attribute come from the same domain
level in the hierarchy are maintained in the global
recording. One advantage in the global recording is
that an anonymous view has uniform domains but it
may lose more information. In our considered
example the global recording for the raw medical
data set is shown in the table 3 it suffers from overgeneralization.
JOB BIRTH POSTCODE ILLNESS
*
*
4350
HIV
*
*
4350
HIV
*
*
5432
Flu
*
*
5432
Fever
*
*
4350
Flu
*
*
4350
fever
TABLE 3: A (0.5, 2)ANONYMOUS TABLE BY
FULL-DOMAIN GENERALIZATION
The local reading the values may be generalized to
different levels in the domain. In our considered
example Table 2 is a 2-anonymous table by local
recoding and in fact one can say that local recoding is
a more general model and global recoding is a special
case of local recoding. We represent the unknown
values by (*) which can be known as the suppression,
which is one special case of generalizations. If
we
observe the table 2 means, it is clear that though it
satisfies 2-anonymity property but it does not protect
two patient information’s i.e. HIV infection. We
cannot distinguish the individual with the first two
tuples. Surely, this is an undesirable outcome. That
this is a problem because the other individual whose
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
generalized identifying attributes are the same as the
mayor also has HIV. The appropriate solution is the
table 4.
JOB BIRTH POSTCODE ILLNESS
*
1975
4350
HIV
*
*
4350
HIV
Cat1
1955
5432
Flu
Cat2
1955
5432
Fever
*
*
4350
Flu
*
1975
4350
fever
TABLE 4: AN ALTERNATIVE 2ANONYMOUS
DATA SET
We see from the above that protection of relationship
to sensitive attribute values is as important as
identification protection. In the case of the privacy
preservation we have two primary goals:
A. To protect individual identification.
B. To protect sensitive relationships.
The aim of our schema is to build the disclosed data
sets. We propose an (α, k)-anonymity model
Where α = Fraction
k = Integer
The frequency for the sensitive values must be
require for the k anomalies which is not less than α in
the any equivalence classes. It is better to extend the
well-known k-anonymity algorithm Incognito to our
(α, k)-anonymity problem. But the algorithm is not
scalable to the size of quasi-identifier and may give a
lot of distortions to the data since it is globalrecoding based, so we also propose an efficient localrecoding based method.
Previously we have seen the work of
association rules for hidden transactional set which is
entirely different from our proposed system. But the
rules to be hidden have to be known beforehand and
each time only one rule can be hidden. Our scheme
blocks all rules from quasi-identifications to a
sensitive class. Even this work is differ from the
work of template-based privacy preservation in
classification problems these are consider hiding
strong associations between some attributes and
sensitive classes and combines k-anonymity with
association hiding. By the suppression we can
ISSN: 2231-5381
provide the solution for aim to minimize the
distortion effect by the Global recording.
The main aim of our schema is the distortions of data
modifications without any attachment to a particular
data mining method such as classification. Hence we
proposed a model to solve the above homogeneity
attack problem named as the (c, l) - diversity model.
The attack assumes that the attacker has background
knowledge to rule out some possible values in a
sensitive attribute for the targeted victim.
Where l = Level of diversity, It will be
more different sensitive values in a group if l is large.
The intensive idea of using parameters c and l is to
ensure that the most frequent sensitive value in a
group should not be too frequent after the next p most
frequent sensitive values are removed, where p is
related to parameter l. We propose to handle the
issues of k-anonymity with protection of sensitive
values for sensitive attributes.
We propose a simple and effective model to
protect both identifications and sensitive associations
in a disclosed data set. Where (α, k)-anonymity has
been extended from the k-anonymity model to limit
the confidence of the implications from the quasiidentifier to a sensitive value (attribute) to within a in
order to protect the sensitive information from being
inferred by strong implications. We extend an
unknown a global-recoding algorithm for the kanonymity problem, to solve this problem for (α, k)anonymity. The proposed local-recoding algorithm is
scalable and generates less distortion. The kanonymity model requires that every value set for the
quasi-identifier attribute set has a frequency of zero
or at least k. Consider a large collection of patient
records with different medical conditions. Some
diseases are sensitive, such as HIV, but many
diseases are common, such as cold and fever.
DEFINITION ((a, k)-ANONYMIZATION). A view
of a table is said to be an (a, k)-anonymization of the
table if the view modifies the table such that the view
satisfies both k-anonymity and a-deassociation
properties with respect to the quasi-identifier.
Table 4 is a (0.5, 2)-anonymous view of the
raw medical data set since the size of all equivalence
classes with respect to the quasi-identifier is 2 and
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each equivalence class contains at most half of the
tuples associating with HIV.
DEFINITION (LOCAL RECODING). Given a data
set D of tuples, a function c that convert each tuple t
in D to c(t) is a local recoding for D.
The tuples in the data sets can be destroyed
by the Local recording. We can define a measurement
for the amount of distortion generated by a recoding,
which we shall call the recoding cost. If the
suppression uses the unknown (*) values is used in
the recording then the total cost is calculated by the
total no. of the suppressions or the number of *’s in
the resulting data set. We can justify it as the optimal
(α, k)-anonymity. The problem decision is defined as
following : (a, k)-ANONYMIZATION: Given a
data set D with a quasi-identifier Q and a sensitive
value s. There a local recoding for D by a function c
such that, after recoding, (a, k)-anonymity is satisfies
and the cost of the recoding is at most C?
GLOBAL-RECODING
Incognito algorithm is an optimal algorithm for the kanonymity problem which can be extended to the
existing global-recoding based algorithm called
Incognito for the (a, k)-anonymous model. It has also
been used in for the l-diversity problem and the make
use of monotonicity property in searching the
solution space. The searches can be made efficient if
a stopping condition is satisfied. The algorithm is
similar to (a, k)-anonymous model. Test for the kanonymity property and tests the k-anonymity and ldiversity properties.
LOCAL-RECODING
The solution may generate excessive distortions to
the data set and may not be scalable by the extended
Incognito algorithm which is an exhaustive global
recoding algorithm. We propose a scalable localrecoding algorithm called top-down approach. The
approach is to tackle the problem for ease of
illustration. The quasi-identifier of size 1 for initially.
Then the method can handle the quasi-identifiers of
size greater than 1. Main proposal of the algorithm is
ISSN: 2231-5381
to generalize all tuples completely. Hence all the
tuples are generalized into the one equivalence class
and these tuples are specialized in the iterations. We
must maintain the (a, k)-anonymous during the
specialization. Up to the occurrence of no
specialization the process is performed continuously.
Quasi-identifier of Size More Than 1:
In the case of the quasi-identifier has a size greater
than one to handle we extend the top-down
algorithm. At the first step all the attributes can be
fully generalized. For each iteration we can find the
“best” attribute for specialization and perform the
specialization for the “best” attribute. For the analysis
consider the group P, and specialize with single
attribute. Our approach for each attribute in the quasiidentifier “tries” to specialize P. We find the “best”
attribute for final specialization among those
specializations.
Paradigm 1 (Greatest No of Tuples Specialized):
During the specialization of P, we obtain a final
distribution of the tuples and some may remain in the
P. If the least no. of distortion is corresponded it is
caused by the “best” specialization yields the greatest
number of tuples specialized.
Paradigm 2 (Smallest No of Branching Specialized):
We want to consider the no. of branches specialized
when there is a tie between the tuples in the first
considered criteria. The smallest no. of branches is
specialized for the “best” specialized yields. The
rationale is that smallest number of branches can be
an indicator of more generalized domain and it is a
good choice compared to a less generalized domain.
EXISTING SYSTEM
Mitigating leakage or misuse incidents of
data stored in databases (i.e., tabular data) by an
insider having legitimate privileges to access the data
is a challenging task. Current Methods devised are
generally based on user behavioral profiles that
define normal user behavior and issue an alert
whenever a user’s behavior significantly deviates
from the normal profile. Security-related data
measures including k-Anonymity, l-Diversity and
(α,k)-Anonymity are mainly used for privacypreserving and are not relevant when the user has free
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
access to the data. The most common approach for
representing user behavioral profiles is by analyzing
the SQL statement submitted by an application server
to the database (as a result of user requests), and
extracting various features from these SQL
statements. The data exposed to the used is attained
by the another approach for the analyzing, i.e., the
result-sets. However these methods ignore the
different sensitivity levels of the data to which an
insider is exposed. The data leaks or misuses in an
organization show the big and great impact of the
damages by the sensitivity factors. So a better system
is required to address these issues.
Mitigating data leakage or misuse is still the
primary focus. The Misusability Weight, which
assigns sensitivity score to datasets, thereby
estimating the level of harm that might be inflicted
upon the organization when the data is leaked. The
misusability can propose the four optional usages:
 The data actually exposed by applying the
anomalies detection by the learning the normal
behavior to an insider in terms of the
sensitivity.
 To handle the leakages, to prevent, we have an
improved process to identify the incidents by
other misuse detection systems by enabling the
security officer to focus on incidents involving
more sensitive data.
 Implementing a dynamic misusability-based
access control, to store the data in the
relational databases is designed to regulate
user access to sensitive data
 Data misusability is reduced.
Assigning a misusability weight to a given dataset is
strongly related to the way the data is presented (e.g.,
tabular data, structured or free text) and is domain
specific. Hence, the measure of misusability weight
cannot fit all types of data in every domain but it
gives a fair idea on how to proceed.




Number of entities
Anonymity level
Number of properties
Values of properties
The M-score incorporates three main factors
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


Quality of data - the importance of the
information.
Quantity data - how much information is
exposed.
The distinguishing factor - given the quasiidentifiers, the amount of efforts required in
order to discover the specific entities that the
table refers to.
Records Ranking
In this approach, the domain expert is requested to
assign a sensitivity score to individual records.
Hence, domain expert expresses the sensitivity level
of different combinations of sensitive values. There
are two challenges when applying the records ranking
method:
a.
choosing a record-set that will make it
possible to derive a general model that will be
as small and compact as possible (since it is
not possible to rank all records in the
database).
b. Choosing an algorithm (knowledge model)
for deriving the scoring model.
Tackling the second challenge is a bit more
complicated because many different methods, each
with its pros and cons, can be chosen for building a
knowledge-model from a list of ranked records.
Among the attributes of the functional dependencies
one of the most prominent difference between
methods of functional dependencies to derive the
function, we examined Records Ranking[LR], Pair
wise Comparison[AHP], Records Ranking[CART]
models for validating Kendall Tau(Accuracy) scores.
Records Ranking [LR] handles unknown values well
with an assumption is made that the relationships
between the attributes and the dependent variable are
linear. Pair wise Comparison [AHP] uses analytic
hierarchy process AHP tree structures and hence
produces optimized results faster. Records
Ranking[CART] makes no assumption that the
relationships between the attributes and the
dependent variable are linear and hence takes much
time. Records Ranking[LR] and Pair wise
Comparison[AHP] significantly outperformed the
Records Ranking[CART] in expert scoring and hence
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
the chosen mode of knowledge model. Knowledge
acquired from one expert is sufficient to calculate the
M-score for the entire domain. The main goal of this
experiment was to find whether the M-score fulfills
its goal of measuring misusability weight. An
implementation of the above approach validates the
current systems efficiency in identifying the potential
data misuse.
PROPOSED SYSTEM
Prior Approaches used one domain expert
for deducting sensitive information. But for better
performance and accuracy we propose to use the
effect of combining knowledge from several experts
(e.g., ensemble of knowledge models). Also we plan
to extend the computations of sensitivity level of
sensitive attributes to be objectively obtained by
using machine learning techniques such as SVM
classifier along with expert scoring models. This
approach particularly by fits the sensitive parameter
values to the customer value based on customer
activity which is far more efficient compared to face
value specification with human involvement.
Consider, an intruder from the outside may
be able to gain unauthorized access to data by
sending carefully crafted queries to a back-end
database of a Web application. The data mining
community has been studied at building strong
privacy-preserving models and designing efficient
optimal and scalable heuristic solutions. An insider
attack against an RDBMS is much more difficult to
detect, and potentially much more dangerous. The
goal of the work reported in our scheme is focus on a
study on the k-anonymity property .The quasiidentifier is the related model for k-anonymity which
is a set of attributes that may serve as an identifier in
the data set. In our survey we have noticed that kanonymization has 2 main models. They are
1. Global recording
2. Local recording
All values of an attribute come from the
same domain level in the hierarchy are maintained in
the global recording. One advantage in the global
recording is that an anonymous view has uniform
domains but it may lose more information. The local
reading the values may be generalized to different
ISSN: 2231-5381
levels in the domain. The aim of our schema is to
build the disclosed data sets. We propose an (α, k)anonymity model
Where α = Fraction
k = Integer
The frequency for the sensitive values must
be require for the k anomalies which is not less than α
in the any equivalence classes. (α, k)-anonymization:
Given a data set D with a quasi-identifier Q and a
sensitive value s. There a local recoding for D by a
function c such that, after recoding, (α, k)-anonymity
is satisfies and the cost of the recoding is at most C.
Incognito algorithm is an optimal algorithm
for the k-anonymity problem which can be extended
to the existing global-recoding based algorithm called
Incognito for the (a, k)-anonymous model. It has also
been used in for the l-diversity problem and the make
use of monotonicity property in searching the
solution space.
The solution may generate excessive
distortions to the data set and may not be scalable by
the extended Incognito algorithm which is an
exhaustive global recoding algorithm. The quasiidentifier of size 1 for initially. Then the method can
handle the quasi-identifiers of size greater than 1.
Main proposal of the algorithm is to generalize all
tuples completely. In the case of the quasi-identifier
has a size greater than one to handle we extend the
top-down algorithm. At the first step all the attributes
can be fully generalized. For each iteration we can
find the” best” attribute for specialization and
perform the specialization for the “best” attribute. For
the analysis consider the group P, and specialize with
single attribute. We find the “best” attribute for final
specialization among those specializations.
SPECULATIVE STUDY
The algorithm was implemented in C/C++.
Pentium IV 2.2GHz PC with 1GM RAM was used to
conduct our experiment. For data set availability,
Adult Database, at the UCIrvine Machine Learning
Repository. Table 5 shows the quasi-identifier chosen
nine attributes. Consider α, k values as 0.5, 2 and
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choose the first 8 attributes and last attributes as the
quasi-identifier and the sensitive attribute
respectively. We can assay the proposed algorithm in
terms of two measurements: execution time and
distortion ratio as we seen in the previous sections.
Attribute
Distinct
Values
Generalization
Height
1
2
Age
74
5,10,20 year ranges 4
Work
7
Taxonomy Tree
3
Class
3 Education
16
Taxonomy Tree
4
4
Marital
7
Taxonomy Tree
3
Status
5 Occupation
14
Taxonomy Tree
2
6
Race
5
Taxonomy Tree
2
7
Gender
2
Suppression
1
8
Native
41
Taxonomy Tree
3
Country
9
Salary
2
Suppression
1
Class
TABLE 7: DESCRIPTION OF ADULT DATA SET
We expressed the proposed algorithms by
Top Down and eIncognito. The eIncognito justifies
the extended Incognito algorithm while Top Down
denotes the local-recoding based top-down approach,
respectively.
(a)
(c)
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(b)
(d)
Fig : Execution Time and Distortion Ratio Versus
Quasiidentifier Size and a (k = 2)
The above figure shows the graphs of the execution
time and the distortion ratio against quasi-identifier
size and a when k = 2. In (a) when α varies, different
algorithms change differently. In (b) the execution
time of the algorithm increases because the
complexity of the algorithms is increased with the
quasi-identifier size, when the quasi-identifier size
increases. In (c) the distortion and α is inversely
propositional, hence when α increases, the distortion
ratio decreases. In (d) it is easy to see why the
distortion ratio increases with the quasi-identifier
size.
Intuitively, if α is greater, there is less
requirement of α -deassociation, to generalize the
values in the data set better to yield few operations by
which we can attain the distortion ratio less in
manner. When the quasi-identifier contains more
attributes, the quasi-identifier has more chance for
having two tuples are not unique. The distortion ratio
is high when the k value is large in size obviously
because it is less likely that the quasi-identifier of two
tuples is equal. The observation in the average Top
Down algorithm results is about 3 times smaller
distortion ratio compared with eIncognito Algorithm.
GENERAL (α, k)-ANONYMITY MODEL
We extend the simple (α, k)-model to
multiple sensitive values, when there are two or more
sensitive values and they are rare cases in a data set.
To combine the two sensitive classes in to one
sensitive class the simple (α, k)-anonymity model is
applicable. The inference confidence controlled by α,
to each individual sensitive value is smaller than or
equal to the confidence to the combined value. It is
possible to have an (α, k) anonymity model to protect
a sensitive attribute when the attribute contains many
values and no single value dominates the attribute.
When same scales holds for each equivalent class
with even distribution, the equivalent class with the
33% of confidence to attain the infer the scale. The αdisassociated for each data set with respect to a
sensitive attribute with respect to every value in
attribute. We can extended the proposed algorithm to
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
the general (α, k)-anonymity model. The
monotonicity property supposed to global-recoding,
to holds for the general (a, k)-anonymity. The topdown local-recoding algorithm can also be easily
extended to the general model by modifying the
condition when testing the candidates.
[6] A. Machanavajjhala, J. Gehrke, and D. Kifer. ldiversity: privacy beyond k-anonymity. In To appear
in ICDE06, 2006.
Authors Profile
CONCLUSION
The identification information is protected
and the sensitive data cannot be protected in the kanonymity model. To protect the both identification
information and sensitive data we propose (α, k)anonymity. We prove that achieving optimal (a, k)anonymity by local recoding is NP-hard. To
transform the data set to satisfy (α, k)-anonymity
property we present an optimal global-recoding
method and an efficient local-encoding based
algorithm. Hence our evolution shows that, on
average, the local-encoding based algorithm performs
about 4 times faster and gives about 3 times less
distortions of the data set compared with the globalrecoding algorithm.
REFERENCE
[1]Raymond Chi-Wing Wong, Jiuyong Li, Ada WaiChee Fu and Ke Wang, “(α, k)-Anonymity: An
Enhanced k-Anonymity Model for PrivacyPreserving Data Publishing”, KDD’06, August 20–
23, 2006.
[2] K. LeFevre, D. J. DeWitt, and R. Ramakrishnan.
Incognito: Efficient full-domain k-anonymity. In
SIGMOD Conference, pages 49–60, 2005.
[3] B. C. M. Fung, K. Wang, and P. S. Yu. Top-down
specialization for information
and privacy preservation. In ICDE, pages 205–216,
2005.
[4] L. Sweeney. Achieving k-anonymity privacy
protection using generalization and suppression.
International journal on uncertainty, Fuzziness and
knowledge based systems, 10(5):571 – 588, 2002.
[5] E. K. C. Blake and C. J. Merz. UCI repository of
machine
learning
databases,
http://www.ics.uci.edu/mlearn/MLRepository.html,
1998.
ISSN: 2231-5381
SRIDEVI. S has completed her P.G degree,
MCA from OsmaniaUniversity in 2003.She has
worked previously as an Assistant Professor in
JMJ
College
for
Women
and
also
in
K.Chandrakala PG College, Tenali.Presently she
is M.Tech student in VVIT College,Nambur
Ramachandran, is a research scholar at Acharya
NagarjunaUniversity, Nambur. He got his B.TECH
Computer
Science
&Systems
engineering
Degreefrom Andhra University andM.TECH in
Computer
ScienceEngineering
from
JNTU,
Kakinada. He is verymuch interested in image
processing, medical retrieval , human vision &
pattern recognition.He did several projects in image
processing.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
Dr Rupa, working as theof the Department of
Computer
Science
and
Engineering
in
VVIT.Nambur.She has 12 yearsof teaching
experience.
Dr
Ch.Rupa,
obtained
her
B.Tech(CSIT) Degree in Computer Science &
Information Technology from JNTU, Hyderabad
in 2002 and M.Tech (IT) in Information
Technology from Andhra University in 2005. She
was awarded Ph. D (CSE) in Computer Science
Engineering by Andhra University.
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