Privacy preservation techniques in data mining

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 04 | Apr-2013, Available @ http://www.ijret.org 537
PRIVACY PRESERVATION TECHNIQUES IN DATA MINING
Jharna Chopra1
, Sampada Satav2
1
M.E Scholar, CTA, 2
Asst. Prof, CSE, SSCET, Bhilai, CG, India,
jharna.chopra@gmail.com, sampada.satav@gmail.com
Abstract
In this paper different privacy preservation techniques are compared. Classification is the most commonly applied data mining
technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large.
Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs
decision tree or neural network-based classification algorithms. The data classification process involves learning and classification.
In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of
the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples . For a fraud detection
application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The
classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination.
The algorithm then encodes these parameters into a model called a classifier
Index Terms: Data Mining, Privacy Preservation, Clustering, Classification Techniques, Naive Bayes.
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1. INTRODUCTION
Data mining is the process of discovering new patterns from
large data sets involving methods at the intersection of
artificial intelligence, machine learning, statistics and database
systems. The goal of data mining is to extract knowledge from
a data set in a human-understandable structure and involves
database and data management, data pre-processing, model
and inference considerations, interestingness metrics,
complexity considerations, post-processing of found structure,
visualization and online updating.
Privacy has become an increasingly important issue in data
mining [5]. Privacy concerns restrict the free flow of
information. Privacy is one of the most important properties of
information. The protection of sensible information has a
relevant role Organization for legal a commercial do not want
to reveal their private database and information. Even the
individual does not want to reveal their personal data to other
than those they give permission to. This implies that revealing
of an instance to be classified may be equivalent to revealing
secret and private information .The protection of sensible
information has a relevant role .Privacy preserving data
mining algorithms have been recently introduced with the aim
of preventing the discovery of sensible information .
Privacy-Preserving Data Mining is developing models without
seeing the data is receiving growing attention [4]. Privacy-
preserving data mining considers the problem of running data
mining algorithms on confidential data that is not supposed to
be revealed even to the party running the algorithm. There are
two significance settings for privacy-preserving data mining In
the first, the data is divided amongst two or more different
parties, and the aim is to run a data mining algorithm on the
union of the parties' databases without allowing any party to
view anyone else's private data. In the second, some statistical
data that is to be released (so that it can be used for research
using statistics and/or data mining) may contain confidential
data and so is first modified so that (a) the data does not
compromise anyone's privacy, and (b) it is still possible to
obtain meaningful results by running data mining algorithms
on the modified data set [3].
This paper address issues related a privacy preserving data
classification and its advantages .Data source collaborate to
develop a global model but the data is not disclosed to others.
Naïve Bayes classifier is used as baseline as it provides
reasonable classification performance.
2. PRIVACY PERSERVATION TECHNIQUES
A). Privacy Preserving Association rule Learning
In data mining, association rule learning is a popular and well
researched method for discovering interesting relations
between variables in large databases. The goal of association
rule learning is to find specific patterns that represent
knowledge in generalized form without referring to particular
data item. Because of this one might say that association rule
learning only represents an indirect threat to privacy. However
traditional methods require access to the data set in order to
be able to find association rules. form without referring to
particular data items.

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B). Privacy preserving Clustering techniques
Clustering can be said as identification of similar classes of
objects. By using clustering techniques we can further identify
dense and sparse regions in object space and can discover
overall distribution pattern and correlations among data
attributes. Classification approach can also be used for
effective means of distinguishing groups or classes of object
but it becomes costly so clustering can be used as
preprocessing approach for attribute subset selection and
classification. The goal in clustering is to partition data
elements into clusters so that the similarity among elements
belonging to the same clusters is high, and so that the
similarity among elements from different clusters is
low. In privacy preserving clustering a main goal is to find the
clusters in the data without revealing the content of the data
elements themselves. the data may be partitioned vertically
and/or horizontally among the involved parties
Types of clustering methods:-
a. Partitioning Methods
b. Hierarchical Agglomerative (divisive)
methods
c. Density based methods
d. Grid-based methods
C). Privacy Preserving Classification Techniques
It is one of the biggest challenges in data mining . It is a
predictive modeling task with the specific aim of predicting
the value of a single nominal variable based on the known
values of other variables[1].Classification is the task of
generalizing known structure to apply to new data.
Classification is a data mining function that assigns items in a
collection to target categories or classes. The goal of
classification is to accurately predict the target class for each
case in the data. A classification task begins with a data set in
which the class assignments are known. Classifications are
discrete and do not imply order. Continuous, floating-point
values would indicate a numerical, rather than a categorical,
target. A predictive model with a numerical target uses a
regression algorithm, not a classification algorithm. The
simplest type of classification problem is binary classification.
In binary classification, the target attribute has only two
possible values: for example, high credit rating or low credit
rating. Multiclass targets have more than two values: for
example, low, medium, high, or unknown credit rating the
model build (training) process, a classification algorithm finds
relationships between the values of the predictors and the
values of the target. Different classification algorithms use
different techniques for finding relationships. These
relationships are summarized in a model, which can then be
applied to a different data set in which the class assignments
are unknown. Classification models are tested by comparing
the predicted values to known target values in a set of test
data. The historical data for a classification project is typically
divided into two data sets: one for building the model; the
other for testing the model. Classification has many
applications in customer segmentation, business modeling,
marketing, credit analysis, and biomedical and drug response
modeling.
Types of Classification Models:-
a) Decision Tree
b) K-.Nearest Neighbors
c) Artificial Neural Network
d) Support Vector Machine
e) Naive Bayes
a). Decision Tree
A decision tree is a classifier expressed as a recursive partition
of the instance space. The decision tree consists of nodes that
form a rooted tree, meaning it is a directed tree with a node
called “root” that has no incoming edges. All other nodes have
exactly one incoming edge. A node with outgoing edges is
called an internal or test node. All other nodes are called
leaves (also known as terminal or decision nodes). In a
decision tree, each internal node splits the instance space into
two or more sub-spaces according to a certain discrete
function of the input attributes values. In the simplest and
most frequent case, each test considers a single attribute, such
that the instance space is partitioned according to the
attribute’s value. In the case of numeric attributes, the
condition refers to a range. Each leaf is assigned to one class
representing the most appropriate target value. Alternatively,
the leaf may hold a probability vector indicating the
probability of the target attribute having a certain value.
Instances are classified by navigating them from the root of
the tree down to a leaf, according to the outcome of the tests
along the path. Internal nodes are represented as circles,
whereas leaves are denoted as tri-angles.
b). K-Nearest Neighbors
The k-nearest neighbor’s algorithm (k-NN) is a method for
classifying objects based on closest training examples in the
feature space. K-NN is a type of instance - based learning, or
we can say it is lazy learning. It can also be used for
regression. The k-nearest neighbour algorithm is amongst the
simplest of all machines –learning algorithms. The space is
partitioned into regions by locations and labels of the training
samples. A point in the space is assigned to the class c if it is
the most frequent class label among the k nearest training
samples. Usually Euclidean distance is used as the distance
metric; however this will only work with numerical values. In
cases such as text classification another metric, such as the
overlap metric (or Hamming distance) can be used.
c) Artificial Neural Network
Neural Networks are analytic techniques modeled after the
(hypothesized) processes of learning in the cognitive system
and the neurological functions of the brain and capable of

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predicting new observations (on specific variables) from other
observations (on the same or other variables) after executing a
process of so-called learning from existing data. Neural
Networks is one of the Data Mining techniques. The first step
is to design a specific network architecture (that includes a
specific number of "layers" each consisting of a certain
number of "neurons "). Network is then subjected to the
process of "training." In the training phase, neurons apply an
iterative process to the number of inputs to adjust the weights
of the network in order to optimally predict the sample data on
which the "training" is performed. After the phase of learning
from an existing data set, the new network is ready and it can
then be used to generate predictions. The resulting “network”
developed in the process of “learning” represents a pattern
detected in the data
d) Support Vector Machine
Support Vector Machines were first introduced to solve the
pattern classification and regression problems by Vapnik and
his colleagues. Support vector machines (SVMs) are a set of
related supervised learning methods used for classification and
regression. Viewing input data as two sets of vectors in an n-
dimensional space, an SVM will construct a separating hyper-
plane in that space, one which maximizes the margin
between the two data sets .To calculate the margin, two
parallel hyper -planes are constructed, one on each side of the
separating hyper-plane, which are "pushed up against" the two
data sets . A good separation is achieved by the hyper -plane
that has the largest distance to the neighbouring data points of
both classes, since in general the larger the margin the lower
the generalization error of the classifier. This hyper -plane is
found by using the support -vectors and margins.
e). Naïve Bayesian
In simple terms, a naive Bayes classifier assumes that the
presence (or absence) of a particular feature of a class is
unrelated to the presence (or absence) of any other feature. For
example, a fruit may be considered to be an apple if it is red,
round, and about 4" in diameter. Even if these features depend
on each other or upon the existence of the other features, a
naive Bayes classifier considers all of these properties to
independently contribute to the probability that this fruit is an
apple. Depending on the precise nature of the probability
model, naive Bayes classifiers can be trained very efficiently
in a supervised learning setting. In many practical
applications, parameter estimation for naive Bayes models
uses the method of maximum likelihood; in other words, one
can work with the naive Bayes model without believing in
Bayesian probability or using any Bayesian methods. In spite
of their naive design and apparently over-simplified
assumptions, naive Bayes classifiers have worked quite well in
many complex real-world situations.

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Table 2.1: Advantage and Disadvantage of different Classification Algorithm
Classification
Algorithm
Advantage Disadvantage
DECISION
TABLE
1)Decision trees are self–explanatory and when
compacted they are also easy to follow. This
representation is considered as comprehensible.
2)Decision trees can handle both nominal and
numeric input attributes.
3)Decision tree representation is rich enough to
represent any discrete value classifier.
4)Decision trees are capable of handling datasets
that may have errors.
5)Decision trees are capable of handling datasets
that have missing Value
1) Most of the algorithms (like ID3
and C4.5) require that the target at-
tribute will have only discrete
values.
2) As decision trees use the “divide
and conquer” method, they tend to
perform well if a few highly relevant
attributes exist, but less so if many
complex interactions are present.
K-.NEAREST
NEIGHBOURS
1)Because the process is transparent, it is easy to
implement and debug.
2)In situations where an explanation of the output
of the classifier is useful k-NN can be very
effective if an analysis of the neighbors is useful
as explanation..
3)There are some noise reduction techniques that
work only for k-NN that can be effective in
improving the
accuracy of the classifier .
1)Because all the work is done at run-
time-KNN can have poor run-time
performance if the training set is
large.
2)k-NN is very sensitive to irrelevant
or
redundant features because all
features
contribute to the similarity and thus
to
the classification. This can be
ameliorated by careful feature
selection or feature weighting.
ARTIFICIAL.N
EURAL
NETWORK
1)Neural network can perform tasks that
a linear program cannot.
2)When an element of the neural network fails, it
can continue without any problem by their
parallel nature.
3) A neural network learns and does not need to be
reprogrammed.
4)It can be implemented in any application and
without any problem.
1)The neural network needs training
to operate.
2)The architecture of a neural
Network is different from the
architecture of microprocessors
therefore needs to be emulated
3)Requires high processing time for
large neural networks.
SUPPORT
VECTOR
MACHINE
1)By introducing the kernel, SVMs gain flexibility
in the
choice of the form of the threshold separating
solvent from insolvent companies, which needs not
be linear and needs not have the same functional
form for all data.
2)SVM deliver a unique solution
1) Lack of transparency of results

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CONCLUSIONS
The objective of our work is to provide a study of different
privacy preservation techniques. The various advantages and
disadvantages are listed on the table. Various techniques and
data mining classifiers are defined in this work which has
emerged in recent years for efficient and effective
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