This document summarizes several techniques for privacy preservation in data mining and machine learning. It reviews algorithms for anonymizing social networks and preserving privacy in association rule mining. It also evaluates approaches for privacy-preserving decision trees, support vector machines, gradient descent methods, and data analysis. Overall, the document surveys and compares various algorithms and methods for protecting sensitive information while still enabling useful data mining and analysis.
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
Privacy Preservation and Restoration of Data Using Unrealized Data SetsIJERA Editor
In today’s world, there is an improved advance in hardware technology which increases the capability to store and record personal data about consumers and individuals. Data mining extracts knowledge to support a variety of areas as marketing, medical diagnosis, weather forecasting, national security etc successfully. Still there is a challenge to extract certain kinds of data without violating the data owners’ privacy. As data mining becomes more enveloping, such privacy concerns are increasing. This gives birth to a new category of data mining method called privacy preserving data mining algorithm (PPDM). The aim of this algorithm is to protect the easily affected information in data from the large amount of data set. The privacy preservation of data set can be expressed in the form of decision tree. This paper proposes a privacy preservation based on data set complement algorithms which store the information of the real dataset. So that the private data can be safe from the unauthorized party, if some portion of the data can be lost, then we can recreate the original data set from the unrealized dataset and the perturbed data set.
SECURED FREQUENT ITEMSET DISCOVERY IN MULTI PARTY DATA ENVIRONMENT FREQUENT I...Editor IJMTER
Security and privacy methods are used to protect the data values. Private data values are secured with
confidentiality and integrity methods. Privacy model hides the individual identity over the public data values.
Sensitive attributes are protected using anonymity methods. Two or more parties have their own private data under
the distributed environment. The parties can collaborate to calculate any function on the union of their data. Secure
Multiparty Computation (SMC) protocols are used in privacy preserving data mining in distributed environments.
Association rule mining techniques are used to fetch frequent patterns.Apriori algorithm is used to mine association
rules in databases. Homogeneous databases share the same schema but hold information on different entities.
Horizontal partition refers the collection of homogeneous databases that are maintained in different parties. Fast
Distributed Mining (FDM) algorithm is an unsecured distributed version of the Apriori algorithm. Kantarcioglu
and Clifton protocol is used for secure mining of association rules in horizontally distributed databases. Unifying
lists of locally Frequent Itemsets Kantarcioglu and Clifton (UniFI-KC) protocol is used for the rule mining process
in partitioned database environment. UniFI-KC protocol is enhanced in two methods for security enhancement.
Secure computation of threshold function algorithm is used to compute the union of private subsets in each of the
interacting players. Set inclusion computation algorithm is used to test the inclusion of an element held by one
player in a subset held by another.The system is improved to support secure rule mining under vertical partitioned
database environment. The subgroup discovery process is adapted for partitioned database environment. The
system can be improved to support generalized association rule mining process. The system is enhanced to control
security leakages in the rule mining process.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
Privacy Preservation and Restoration of Data Using Unrealized Data SetsIJERA Editor
In today’s world, there is an improved advance in hardware technology which increases the capability to store and record personal data about consumers and individuals. Data mining extracts knowledge to support a variety of areas as marketing, medical diagnosis, weather forecasting, national security etc successfully. Still there is a challenge to extract certain kinds of data without violating the data owners’ privacy. As data mining becomes more enveloping, such privacy concerns are increasing. This gives birth to a new category of data mining method called privacy preserving data mining algorithm (PPDM). The aim of this algorithm is to protect the easily affected information in data from the large amount of data set. The privacy preservation of data set can be expressed in the form of decision tree. This paper proposes a privacy preservation based on data set complement algorithms which store the information of the real dataset. So that the private data can be safe from the unauthorized party, if some portion of the data can be lost, then we can recreate the original data set from the unrealized dataset and the perturbed data set.
SECURED FREQUENT ITEMSET DISCOVERY IN MULTI PARTY DATA ENVIRONMENT FREQUENT I...Editor IJMTER
Security and privacy methods are used to protect the data values. Private data values are secured with
confidentiality and integrity methods. Privacy model hides the individual identity over the public data values.
Sensitive attributes are protected using anonymity methods. Two or more parties have their own private data under
the distributed environment. The parties can collaborate to calculate any function on the union of their data. Secure
Multiparty Computation (SMC) protocols are used in privacy preserving data mining in distributed environments.
Association rule mining techniques are used to fetch frequent patterns.Apriori algorithm is used to mine association
rules in databases. Homogeneous databases share the same schema but hold information on different entities.
Horizontal partition refers the collection of homogeneous databases that are maintained in different parties. Fast
Distributed Mining (FDM) algorithm is an unsecured distributed version of the Apriori algorithm. Kantarcioglu
and Clifton protocol is used for secure mining of association rules in horizontally distributed databases. Unifying
lists of locally Frequent Itemsets Kantarcioglu and Clifton (UniFI-KC) protocol is used for the rule mining process
in partitioned database environment. UniFI-KC protocol is enhanced in two methods for security enhancement.
Secure computation of threshold function algorithm is used to compute the union of private subsets in each of the
interacting players. Set inclusion computation algorithm is used to test the inclusion of an element held by one
player in a subset held by another.The system is improved to support secure rule mining under vertical partitioned
database environment. The subgroup discovery process is adapted for partitioned database environment. The
system can be improved to support generalized association rule mining process. The system is enhanced to control
security leakages in the rule mining process.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
https://utilitasmathematica.com/index.php/Index
Our journal has academic and professional communities fosters collaboration and knowledge sharing. When all voices are heard and respected, it strengthens the collective capabilities of the statistical community.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
A Frame Work for Ontological Privacy Preserved MiningIJNSA Journal
Data Mining analyses the stocked data and helps in foretelling the future trends. There are different techniques by which data can be mined. These different techniques reveal different types of hidden knowledge. Using the right procedure of technique result specific patterns emerge. Ontology is a specification of conceptualization. It is a description of concepts and relationships that can exist for an agent or a community of agents. To make software more user-friendly, ontology could be used to explain both the technical and domain details. In the process of analyzing a data certain important details cannot be revealed, therefore security is the most important feature dealt in all technologies and work places. Data mining and Ontology techniques when integrated would capitulate an efficient system capable of selecting the appropriate algorithm for a data mining technique and privacy preserving techniques also by exploring the domain knowledge using ontology.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
Data perturbation is one of the most popular models used in pr
ivacy preserving data mining. It is specially
convenient for applications where the data owners need to export/publi
sh the privacy-sensitive data. This
work proposes that an Additive Perturbation based Privacy Pre
serving Data Mining (PPDM) to deal with
the problem of increasing accurate models about all data without
knowing exact details of individual
values. To Preserve Privacy, the approach establishes R
andom Perturbation to individual values before
data are published. In Proposed system the PPDM approach introd
uces Multilevel Trust (MLT) on data
miners. Here different perturbed copies of the similar data a
re available to the data miner at different trust
levels and may mingle these copies to jointly gather extra infor
mation about original data and release the
data is called diversity attack. To prevent this attack ML
T-PPDM approach is used along with the addition
of random Gaussian noise and the noise is properly correlated to
the original data, so the data miners
cannot get diversity gain in their combined reconstruction.
Misusability Measure Based Sanitization of Big Data for Privacy Preserving Ma...IJECEIAES
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.
Privacy Preserving in Cloud Using Distinctive Elliptic Curve Cryptosystem (DECC)ElavarasaN GanesaN
Securing the data over a cloud network is always a challenging problem
for the researcher over the past one decade. There exist many conventional
algorithms/techniques which proclaim to ensure secure transmission, storage
and retrieval of data over the cloud platform. All these mechanisms mainly
focus on ensuring privacy preserve for of client / user‟s data. This research
work aims to propose distinctive elliptic curve cryptography. DECC is based
on the algebraic structure of elliptic curves in the finite fields. The DECC is
used for privacy preserving since smaller keys are used when compared to all
the rest of the existing cryptographic algorithms. Performance metrics such as
average relative error, time, anonymization time and information loss are
taken into account. Implementations are carried out in MATLAB tool. Results
portrays that the proposed DECC outperforms the existing methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Users Approach on Providing Feedback for Smart Home Devices – Phase IIijujournal
Smart Home technology has accomplished extraordinary success in making individuals' lives more straightforward and relaxing. Technology has recently brought about numerous savvy and refined frame works that advanced clever living innovation. In this paper, we will investigate the behavioral intention of user's approach to providing feedback for smart home devices. We will conduct an online survey for a sample of three to five students selected by simple random sampling to study the user's motto for giving feedback on smart home devices and their expectations. We have observed that most users are ready to actively share their input on smart home devices to improve the product's service and quality to fulfill the user’s needs and make their lives easier.
Users Approach on Providing Feedback for Smart Home Devices – Phase IIijujournal
Smart Home technology has accomplished extraordinary success in making individuals' lives more
straightforward and relaxing. Technology has recently brought about numerous savvy and refined frame
works that advanced clever living innovation. In this paper, we will investigate the behavioral intention of
user's approach to providing feedback for smart home devices. We will conduct an online survey for a
sample of three to five students selected by simple random sampling to study the user's motto for giving
feedback on smart home devices and their expectations. We have observed that most users are ready to
actively share their input on smart home devices to improve the product's service and quality to fulfill the
user’s needs and make their lives easier.
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TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
https://utilitasmathematica.com/index.php/Index
Our journal has academic and professional communities fosters collaboration and knowledge sharing. When all voices are heard and respected, it strengthens the collective capabilities of the statistical community.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
A Frame Work for Ontological Privacy Preserved MiningIJNSA Journal
Data Mining analyses the stocked data and helps in foretelling the future trends. There are different techniques by which data can be mined. These different techniques reveal different types of hidden knowledge. Using the right procedure of technique result specific patterns emerge. Ontology is a specification of conceptualization. It is a description of concepts and relationships that can exist for an agent or a community of agents. To make software more user-friendly, ontology could be used to explain both the technical and domain details. In the process of analyzing a data certain important details cannot be revealed, therefore security is the most important feature dealt in all technologies and work places. Data mining and Ontology techniques when integrated would capitulate an efficient system capable of selecting the appropriate algorithm for a data mining technique and privacy preserving techniques also by exploring the domain knowledge using ontology.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
Data perturbation is one of the most popular models used in pr
ivacy preserving data mining. It is specially
convenient for applications where the data owners need to export/publi
sh the privacy-sensitive data. This
work proposes that an Additive Perturbation based Privacy Pre
serving Data Mining (PPDM) to deal with
the problem of increasing accurate models about all data without
knowing exact details of individual
values. To Preserve Privacy, the approach establishes R
andom Perturbation to individual values before
data are published. In Proposed system the PPDM approach introd
uces Multilevel Trust (MLT) on data
miners. Here different perturbed copies of the similar data a
re available to the data miner at different trust
levels and may mingle these copies to jointly gather extra infor
mation about original data and release the
data is called diversity attack. To prevent this attack ML
T-PPDM approach is used along with the addition
of random Gaussian noise and the noise is properly correlated to
the original data, so the data miners
cannot get diversity gain in their combined reconstruction.
Misusability Measure Based Sanitization of Big Data for Privacy Preserving Ma...IJECEIAES
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.
Privacy Preserving in Cloud Using Distinctive Elliptic Curve Cryptosystem (DECC)ElavarasaN GanesaN
Securing the data over a cloud network is always a challenging problem
for the researcher over the past one decade. There exist many conventional
algorithms/techniques which proclaim to ensure secure transmission, storage
and retrieval of data over the cloud platform. All these mechanisms mainly
focus on ensuring privacy preserve for of client / user‟s data. This research
work aims to propose distinctive elliptic curve cryptography. DECC is based
on the algebraic structure of elliptic curves in the finite fields. The DECC is
used for privacy preserving since smaller keys are used when compared to all
the rest of the existing cryptographic algorithms. Performance metrics such as
average relative error, time, anonymization time and information loss are
taken into account. Implementations are carried out in MATLAB tool. Results
portrays that the proposed DECC outperforms the existing methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
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Users Approach on Providing Feedback for Smart Home Devices – Phase IIijujournal
Smart Home technology has accomplished extraordinary success in making individuals' lives more straightforward and relaxing. Technology has recently brought about numerous savvy and refined frame works that advanced clever living innovation. In this paper, we will investigate the behavioral intention of user's approach to providing feedback for smart home devices. We will conduct an online survey for a sample of three to five students selected by simple random sampling to study the user's motto for giving feedback on smart home devices and their expectations. We have observed that most users are ready to actively share their input on smart home devices to improve the product's service and quality to fulfill the user’s needs and make their lives easier.
Users Approach on Providing Feedback for Smart Home Devices – Phase IIijujournal
Smart Home technology has accomplished extraordinary success in making individuals' lives more
straightforward and relaxing. Technology has recently brought about numerous savvy and refined frame
works that advanced clever living innovation. In this paper, we will investigate the behavioral intention of
user's approach to providing feedback for smart home devices. We will conduct an online survey for a
sample of three to five students selected by simple random sampling to study the user's motto for giving
feedback on smart home devices and their expectations. We have observed that most users are ready to
actively share their input on smart home devices to improve the product's service and quality to fulfill the
user’s needs and make their lives easier.
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large (probably) prime numbers using its own software.
A novel integrated approach for handling anomalies in RFID dataijujournal
Radio Frequency Identification (RFID) is a convenient technology employed in various applications. The
success of these RFID applications depends heavily on the quality of the data stream generated by RFID
readers. Due to various anomalies found predominantly in RFID data it limits the widespread adoption of
this technology. Our work is to eliminate the anomalies present in RFID data in an effective manner so that
it can be applied for high end applications. Our approach is a hybrid approach of middleware and
deferred because it is not always possible to remove all anomalies and redundancies in middleware. The
processing of other anomalies is deferred until the query time and cleaned by business rules. Experimental
results show that the proposed approach performs the cleaning in an effective manner compared to the
existing approaches.
UBIQUITOUS HEALTHCARE MONITORING SYSTEM USING INTEGRATED TRIAXIAL ACCELEROMET...ijujournal
Ubiquitous healthcare has become one of the prominent areas of research inorder to address the
challenges encountered in healthcare environment. In contribution to this area, this study developed a
system prototype that recommends diagonostic services based on physiological data collected in real time
from a distant patient. The prototype uses WBAN body sensors to be worn by the individual and an android
smart phone as a personal server. Physiological data is collected and uploaded to a Medical Health
Server (MHS) via GPRS/internet to be analysed. Our implemented prototype monitors the activity, location
and physiological data such as SpO2 and Heart Rate (HR) of the elderly and patients in rehabilitation. The
uploaded information can be accessed in real time by medical practitioners through a web application.
ENHANCING INDEPENDENT SENIOR LIVING THROUGH SMART HOME TECHNOLOGIESijujournal
The population of elderly folks is ballooning worldwide as people live longer. But getting older often
means declining health and trouble living solo. Smart home tech could keep an eye on old folks and get
help quickly when needed so they can stay independent. This paper looks at a system combining wireless
sensors, video watches, automation, resident monitoring, emergency detection, and remote access. Sensors
track health signs, activities, appliance use. Video analytics spot odd stuff like falls. Sensor fusion and
machine learning find normal patterns so wonks can see unhealthy changes and send alerts. Multi-channel
alerts reach caregivers and emergency folks. A LabVIEW can integrate devices and enables local and
remote oversight and can control and handle emergency responses. Benefits seem to be early illness clues,
quick help, less burden on caregivers, and optimized home settings. But will old folks use all this tech? Can
we prove it really helps folks live longer and better? More research on maximizing reliability and
evaluating real-world impacts is needed. But designed thoughtfully, smart homes could may profoundly
improve the aging experience.
HMR LOG ANALYZER: ANALYZE WEB APPLICATION LOGS OVER HADOOP MAPREDUCEijujournal
In today’s Internet world, log file analysis is becoming a necessary task for analyzing the customer’s
behavior in order to improve advertising and sales as well as for datasets like environment, medical,
banking system it is important to analyze the log data to get required knowledge from it. Web mining is the
process of discovering the knowledge from the web data. Log files are getting generated very fast at the
rate of 1-10 Mb/s per machine, a single data center can generate tens of terabytes of log data in a day.
These datasets are huge. In order to analyze such large datasets we need parallel processing system and
reliable data storage mechanism. Virtual database system is an effective solution for integrating the data
but it becomes inefficient for large datasets. The Hadoop framework provides reliable data storage by
Hadoop Distributed File System and MapReduce programming model which is a parallel processing
system for large datasets. Hadoop distributed file system breaks up input data and sends fractions of the
original data to several machines in hadoop cluster to hold blocks of data. This mechanism helps to
process log data in parallel using all the machines in the hadoop cluster and computes result efficiently.
The dominant approach provided by hadoop to “Store first query later”, loads the data to the Hadoop
Distributed File System and then executes queries written in Pig Latin. This approach reduces the response
time as well as the load on to the end system. This paper proposes a log analysis system using Hadoop
MapReduce which will provide accurate results in minimum response time.
SERVICE DISCOVERY – A SURVEY AND COMPARISONijujournal
With the increasing number of services in the internet, companies’ intranets, and home networks: service
discovery becomes an integral part of modern networked system. This paper provides a comprehensive
survey of major solutions for service discovery. We cover techniques and features used in existing systems.
Although a few survey articles have been published on this object, our contribution focuses on comparing
and analyzing surveyed solutions according eight prime criteria, which we have defined before. This
comparison will be helpful to determine limits of existing discovery protocols and identify future research
opportunities in service discovery.
SIX DEGREES OF SEPARATION TO IMPROVE ROUTING IN OPPORTUNISTIC NETWORKSijujournal
Opportunistic Networks are able to exploit social behavior to create connectivity opportunities. This
paradigm uses pair-wise contacts for routing messages between nodes. In this context we investigated if the
“six degrees of separation” conjecture of small-world networks can be used as a basis to route messages in
Opportunistic Networks. We propose a simple approach for routing that outperforms some popular
protocols in simulations that are carried out with real world traces using ONE simulator. We conclude that
static graph models are not suitable for underlay routing approaches in highly dynamic networks like
Opportunistic Networks without taking account of temporal factors such as time, duration and frequency of
previous encounters.
International Journal of Ubiquitous Computing (IJU)ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
PERVASIVE COMPUTING APPLIED TO THE CARE OF PATIENTS WITH DEMENTIA IN HOMECARE...ijujournal
The aging population and the consequent increase in the incidence of dementias is causing many
challenges to health systems, mainly related to infrastructure, low services quality and high costs. One
solution is to provide the care at house of the patient, through of home care services. However, it is not a
trivial task, since a patient with dementia requires constant care and monitoring from a caregiver, who
suffers physical and emotional overload. In this context, this work presents an modelling for development of
pervasive systems aimed at helping the care of these patients in order to lessen the burden of the caregiver
while the patient continue to receive the necessary care.
A proposed Novel Approach for Sentiment Analysis and Opinion Miningijujournal
as the people are being dependent on internet the requirement of user view analysis is increasing
exponentially. Customer posts their experience and opinion about the product policy and services. But,
because of the massive volume of reviews, customers can’t read all reviews. In order to solve this problem,
a lot of research is being carried out in Opinion Mining. In order to solve this problem, a lot of research is
being carried out in Opinion Mining. Through the Opinion Mining, we can know about contents of whole
product reviews, Blogs are websites that allow one or more individuals to write about things they want to
share with other The valuable data contained in posts from a large number of users across geographic,
demographic and cultural boundaries provide a rich data source not only for commercial exploitation but
also for psychological & sociopolitical research. This paper tries to demonstrate the plausibility of the idea
through our clustering and classifying opinion mining experiment on analysis of blog posts on recent
product policy and services reviews. We are proposing a Nobel approach for analyzing the Review for the
customer opinion
International Journal of Ubiquitous Computing (IJU)ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
USABILITY ENGINEERING OF GAMES: A COMPARATIVE ANALYSIS OF MEASURING EXCITEMEN...ijujournal
Usability engineering and usability testing are concepts that continue to evolve. Interesting research studies
and new ideas come up every now and then. This paper tests the hypothesis of using an EDA-based
physiological measurements as a usability testing tool by considering three measures; which are observers‟
opinions, self-reported data and EDA-based physiological sensor data. These data were analyzed
comparatively and statistically. It concludes by discussing the findings that has been obtained from those
subjective and objective measures, which partially supports the hypothesis.
SECURED SMART SYSTEM DESING IN PERVASIVE COMPUTING ENVIRONMENT USING VCSijujournal
Ubiquitous Computing uses mobile phones or tiny devices for application development with sensors
embedded in mobile phones. The information generated by these devices is a big task in collection and
storage. For further, the data transmission to the intended destination is delay tolerant. In this paper, we
made an attempt to propose a new security algorithm for providing security to Pervasive Computing
Environment (PCE) system using Public-key Encryption (PKE) algorithm, Biometric Security (BS)
algorithm and Visual Cryptography Scheme (VCS) algorithm. In the proposed PCE monitoring system it
automates various home appliances using VCS and also provides security against intrusion using Zigbee
IEEE 802.15.4 based Sensor Network, GSM and Wi-Fi networks are embedded through a standard Home
gateway.
PERFORMANCE COMPARISON OF ROUTING PROTOCOLS IN MOBILE AD HOC NETWORKSijujournal
Routing protocols have an important role in any Mobile Ad Hoc Network (MANET). Researchers have
elaborated several routing protocols that possess different performance levels. In this paper we give a
performance evaluation of AODV, DSR, DSDV, OLSR and DYMO routing protocols in Mobile Ad Hoc
Networks (MANETS) to determine the best in different scenarios. We analyse these MANET routing
protocols by using NS-2 simulator. We specify how the Number of Nodes parameter influences their
performance. In this study, performance is calculated in terms of Packet Delivery Ratio, Average End to
End Delay, Normalised Routing Load and Average Throughput.
Optical Character Recognition (OCR) is a technique, used to convert scanned image into editable text
format. Many different types of Optical Character Recognition (OCR) tools are commercially available
today; it is a useful and popular method for different types of applications. OCR can predict the accurate
result depends on text pre-processing and segmentation algorithms. Image quality is one of the most
important factors that improve quality of recognition in performing OCR tools. Images can be processed
independently (.png, .jpg, and .gif files) or in multi-page PDF documents (.pdf). The primary objective of
this work is to provide the overview of various Optical Character Recognition (OCR) tools and analyses of
their performance by applying the two factors of OCR tool performance i.e. accuracy and error rate.
Optical Character Recognition (OCR) is a technique, used to convert scanned image into editable text
format. Many different types of Optical Character Recognition (OCR) tools are commercially available
today; it is a useful and popular method for different types of applications. OCR can predict the accurate
result depends on text pre-processing and segmentation algorithms. Image quality is one of the most
important factors that improve quality of recognition in performing OCR tools. Images can be processed
independently (.png, .jpg, and .gif files) or in multi-page PDF documents (.pdf). The primary objective of
this work is to provide the overview of various Optical Character Recognition (OCR) tools and analyses of
their performance by applying the two factors of OCR tool performance i.e. accuracy and error rate.
DETERMINING THE NETWORK THROUGHPUT AND FLOW RATE USING GSR AND AAL2Rijujournal
In multi-radio wireless mesh networks, one node is eligible to transmit packets over multiple channels to
different destination nodes simultaneously. This feature of multi-radio wireless mesh network makes high
throughput for the network and increase the chance for multi path routing. This is because the multiple
channel availability for transmission decreases the probability of the most elegant problem called as
interference problem which is either of interflow and intraflow type. For avoiding the problem like
interference and maintaining the constant network performance or increasing the performance the WMN
need to consider the packet aggregation and packet forwarding. Packet aggregation is process of collecting
several packets ready for transmission and sending them to the intended recipient through the channel,
while the packet forwarding holds the hop-by-hop routing. But choosing the correct path among different
available multiple paths is most the important factor in the both case for a routing algorithm. Hence the
most challenging factor is to determine a forwarding strategy which will provide the schedule for each
node for transmission within the channel. In this research work we have tried to implement two forwarding
strategies for the multi path multi radio WMN as the approximate solution for the above said problem. We
have implemented Global State Routing (GSR) which will consider the packet forwarding concept and
Aggregation Aware Layer 2 Routing (AAL2R) which considers the both concept i.e. both packet forwarding
and packet aggregation. After the successful implementation the network performance has been measured
by means of simulation study.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
1. 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. International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
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. International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
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. International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
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. International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
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. International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
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
[1] Tamir Tassa and Dror J. Cohen”Anonymization Of Centralized And Distributed Social Networks By
Sequential Clustering”, IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 2,
February 2013.
[2] Keng-Pei Lin and Ming-Syan Chen, “On The Design And Analysis Of The Privacy-Preserving SVM
Classifier”, IEEE Transactions On Knowledge And Data Engineering, Vol. 23, No. 11, November
2011.
[3] Haoliang Lou, Yunlong Ma, Feng Zhang, Min Liu, Weiming Shen “Data Mining For Privacy
Preserving Association Rules Based On Improved Mask Algorithm” ,Proceedings Of The 2014 IEEE
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7
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