A review on privacy preservation in data mining

International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
DOI:10.5121/iju.2016.7301 1
A Review on Privacy Preservation in Data Mining
1
T.Nandhini, 2
D. Vanathi, 3
Dr.P.Sengottuvelan
1
M.E.Scholar, Department of Computer Science & Engineering, Nandha Engineering
College, Erode-638052, Tamil Nadu, India
2
Associate Professor, Department of Computer Science & Engineering, Nandha
Engineering College, Erode-638052, Tamil Nadu, India
3
Associate Professor & Head, Department Of Computer Science,
Periyar University PG Extension Cente, Darmapuri
ABSTRACT
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
KEYWORDS
Data mining, privacy preserving, Anonymization
1. INTRODUCTION
The huge amount of data available in information databases becomes worthless until the useful
information is extracted. Mining knowledge from the data is said to be data mining. The two steps
are analyse and extract useful information from database is mandatory for further use in different
work environments like market analysis, fraud detection, science exploration, etc. Information
extraction carried out the following duties such as cleaning data, integration of data,
transformation of data, pattern evaluation, and data presentation. The boom of data mining relies
on the availability of high in data quality and effective sharing. The figure 1 explains the process
of privacy preservation technique.
Figure 1 privacy preservation technique

2
The data mining works with generation of association rules, the modification in support and
confidence of the association rule for masking sensitive rules is done. A concept named „not
altering the support‟ is deployed to hide an association rule. There are two approaches in privacy
preserving data mining. The data Perturbing values for preservation of customer privacy is the
first approach. The other approach is Cryptographic tools to build data mining models. Privacy
preserving [12] is said to be worked out when the attacker is not able to learn anything extra from
the given data even though with the presence of his background knowledge obtained from other
sources.
2. OVERVIEW
Privacy preservation
The main focus of privacy preserving data publishing was to enhance traditional data mining
techniques which mask the sensitive information by modifying the data. The major issues were
how to modify the data and how to rediscover the data mining result from the modified data. The
data Perturbing values for preservation of customer privacy is the first approach. The other
approach is Cryptographic tools to build data mining models. Privacy preserving [12] is preferred
to be go out when the attacker is unable to know anything extra from the given data even though
with the presence of his background knowledge obtained from other sources.
Anonymization
Anonymization of the data is done by hiding the identity of record owners, whereas privacy
preserving data mining seeks to directly belie the sensitive data. The problem of privacy-
preservation in social networks is a major problem. The goal is to arrive at an anonymized view
of the network which is unified without flat out to any of the data holder‟s information apropos
links amid nodes that are controlled by other data holders. The anonymization algorithm and
SaNGreeA algorithm [1] used for sequential clustering. Anonymity parameters are used for
sequential clustering algorithms for anonymizing social networks.
3. LITERATURE SURVEY
Sequential Clustering for Anonymization of Centralized and Distributed Social Networks
The complication of privacy-preservation in social networks is a major problem. The goal is to
arrive at an anonymized view of the unified network without eloquent to any of the data holder‟s
information about links between nodes that are controlled by other data holders. The
anonymization algorithm and SaNGreeA algorithm [1] used for sequential clustering. Anonymity
parameters are used for anonymizing social networks by using sequential clustering algorithms.
Several algorithms produce anonymizations by means of clustering which have an efficient utility
than those achieved by existing algorithms.
On the Design and Analysis of the Privacy-Preserving SVM Classifier
SVM classifier without exposing the private content of training data is preferably said as Privacy-
Preserving SVM Classifier [2]. Data mining algorithm, Classification classifier for public use or
deliver the SVM classifier to clients will bare the private content of support vectors. This violates
the privacy-preserving needs for some legal or commercial account. Privacy violation problem,
and propose an approach as a base technique for the SVM classifier to revamp it to a privacy-
preserving classifier which does not announce the private content of support vectors.

3
Improved MASK Algorithm for Privacy Preserving Association Rules on Data Mining
A data perturbation strategy is implemented through the MASK algorithm, which leads to a
debased privacy-preserving degree. In a while, it is challenging to handle the MASK algorithm
into real time due to long execution time. A hybrid algorithm encapsulated with data perturbation
and query restriction (DPQR) [3] to maximize the privacy-preserving degree by multi-parameters
perturbation. Data Perturbation and Query Restriction (DPQR) algorithm are used to improve
privacy-preserving degree and time-efficiency is achieved. The proposed DPQR is more suitable
for Boolean data, and it cannot deal with numerical data or other types of data.
Privacy-Preserving Gradient-Descent Methods
Gradient descent [4] aims to minimize a target function in order to reach a local trace. In data
mining, this function accords to a decision model that is to be discovered. The author present two
technical approaches stochastic approach and least square approach. Languages modeling
smoothing parameters, weight parameter are used to measure the performance of the system. The
proposed secure building blocks are scalable and the proposed protocols permit us to determine
an efficient secure protocol for the applications for each scenario.The author will extend PPGD to
vertically partitioned data implementing the least square approach for N-number of parities.
Crowd sourcing Database for K-Anonymity
Author suggested integrating the crowdsourcing techniques [5] into the database engine. It
addresses the privacy concern, as each crowdsourcing job requires revealing of some sensitive
data to the anonymous human trader. In this paper, the study focused how to guarantee the data
privacy in the crowdsourcing scenario. A probability-based matrix model is inaugurated to
estimate the lower bound and upper bound of the crowdsourcing certainty for the anonymized
data. The model exhibits that K-Anonymity approach needs to solve the trade-off between the
privacy and the accuracy. Propose a novel K-Anonymity approach. Experiments show that the
solution can cultivate high accuracy results for the crowdsourcing jobs.
Privacy Preserving Decision Tree Learning Using Unrealized Data Sets
Author suggested a privacy preserving approach that can be applied to decision tree learning [6],
without loss of accuracy. It deploys the strategies to the preservation of the privacy of collected
data samples. It converts the original sample data sets into a group of unreal data sets, from which
the original samples cannot be, reestablish without the entire group of unreal data sets. In a while
an unreal data sets which directly built an accurate decision tree. It can be applied directly to the
stored data as soon as the first sample is collected. The approach is better than the other privacy
preserving approaches, such as cryptography, for extra protection.
Traffic Information Systems Based On Secure and Privacy-Preserving Smartphone
Author leverage state-of-the-art cryptographic schemes [7] and readily available
telecommunication infrastructure and presented a comprehensive outperform for traffic
estimation on smartphone that is tried and true to be secure and privacy preserving. A localization
algorithm, suitable for GPS location samples, and evaluated it through realistic simulations.
Results confirm it is attainable to build accurate and trustworthy smartphone-based TIS.

4
A Data Mining Perspective in Privacy Preserving Data Mining Systems
The PPDM systems deployed the key exchange process by cryptographic manner and the key
computation process accomplished by a third party. The Key Distribution-Less Privacy
Preserving Data Mining (KDLPPDM) [9] system is designed. The system novelty is that no data
is published in a same while the association rules are reported to achieve effective data mining
results. Commutative RSA cryptographic algorithms are suggested for key exchanging process. It
overcomes the sustentation arising due to key exchange and key computation by applying the
cryptographic algorithm.
Privacy-Preserving Data Analysis
The existing PPDA techniques [12] cannot prevent participating parties from modifying their
private inputs. It is difficult to check whether the parties participating are reliable about their
private input data. Proposed model first develop key theorems, then based on these theorems,
they analyze certain important privacy-preserving data analysis tasks that telling the truth is the
optimized opinion for any participating party. Deterministically non-cooperatively computable
(DNCC) parameter used for measure the system performance. Claim 5.1, as long as the last step
in a PPDA task is in DNCC, it is always possible to make the entire PPDA task satisfying the
DNCC model.
Random Nonlinear Data Distortion for Privacy-Preserving Outlier Detection
The data owner has some private or sensitive data and needs a data miner to access them for
speculating important patterns by which the sensitive information [20] is not revealed. Privacy-
preserving data mining desired to solve this problem by transforming randomly the data prior to
be allowed to the data miners. Previous works only focused towards the case of linear data
perturbations. Author defines nonlinear data distortion through nonlinear random data
transformation.
4. COMPARISONS ON DIFFERENT PRIVACY PRESERVATION TECHNIQUES
TITLE ALGORITHM PARAMETER CONCLUSION
Anonymization of
Centralized and
Distributed Social
Networks by
Sequential Clustering
Anonymization
algorithm and
SaNGreeA
algorithm used for
sequential
clustering
Clustering coefficient,
Diameter,Average distance,
Effective diameter,
Epidemic threshold.
The presented sequential
clustering algorithms for
anonymizing social
networks. Those
algorithms produce
anonymizations by
means of clustering with
better utility.
Data Mining for
Privacy Preserving
Association Rules
Basedon Improved
MASK Algorithm
Data Perturbation
and
QueryRrestriction
(DPQR)
Multi-parameters
perturbation
The privacy-preserving
degree and time-
efficiency is achieved.
The DPQR is more
suitable for Boolean
data.

5
K-Anonymity for
Crowdsourcing
Database
K-Anonymity
algorithm
No. Of Tuples And Data
spaces are used for measure
the performance of the
system.
The Outperforms
standard K-Anonymity
approaches on retaining
the effectiveness of
crowdsourcing.
Privacy Preserving
Decision Tree
Learning
Using Unrealized
Data Sets
Tree learning
Algorithm,
decision tree
generation are
used.
Temperature
Humidity,
Wind
Play.
The decision tree
algorithm is compatible
with other privacy
preserving approaches,
such as cryptography, for
extra protection.
Secure and Privacy-
Preserving
Smartphone-Based
Traffic Information
Systems
KeyGen(n)
algorithm
GSC(group signature
center)Accuracy,Simulation,
time stamp
A localization algorithm,
suitable for GPS location
samples, and evaluated it
through realistic
simulations.
On the Design and
Analysis of the
Privacy-Preserving
SVM Classifier
Data mining
algorithm,
Classification
algorithm, kernal
adatron algorithm
and datafly
algorithm.
Cost parameter,
Kernalparameter are used to
measure the performance of
the system.
PPSVC can achieve
similar classification
accuracy to the original
SVM classifier. By
protecting the sensitive
content of support
vectors.
Privacy-Preserving
Gradient-Descent
Methods
Genetic
Algorithms
Languages modeling
smoothing parameters,
weight parameters are used
to measure the performance
of the system.
The secure building
blocks are scalable and
the proposed protocols
allow us to determine a
better secure protocol for
the applications for each
scenario.
A Data Mining
Perspective in
Privacy Preserving
Data Mining Systems
1.C5.0 data
mining algorithm,
Commutative
RSA
cryptographic
algorithm.
Area covered by roc,
curve data set id, sensitivity,
specificity-1
Overcomes the
overheads arising due to
key exchange and key
computation by adopting
the cryptographic
algorithm.
Incentive Compatible
Privacy-Preserving
Data Analysis
Data analysis
algorithms
Deterministically non-
cooperatively computable
(DNCC).
Claim 5.1, as long as the
last step in a PPDA task
is in DNCC, it is always
possible to make the
entire PPDA task
satisfying the DNCC
model.

6
Privacy and Quality
Preserving
Multimedia Data
Aggregation for
Participatory Sensing
Systems
Outlier detection
anomaly
detection
algorithm, secure
hash algorithm.
Detection rate, data range.
Indices, anomaly score
A general method for
computing the bounds on
a nonlinear privacy-
preserving data-mining
technique with
applications to anomaly
detection.
5. CONCLUSION
Review on data mining privacy preserving in social network. Main objective of this review on
privacy preservative technique is to protect different users and their identities in the social
network along with obtaining originality. To achieve this goal, there is a need to develop perfect
privacy models to specify the expected loss of privacy under different attacks, and deployed
anonymization techniques to the data. So, the various techniques are surveyed.
REFERENCES
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7
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