This document proposes a system to preserve privacy in data mining using an inverse frequent itemset mining approach. It discusses limitations in existing privacy preserving data mining techniques. The proposed system applies data transformation techniques like encryption before data mining. It then uses an inverse frequent itemset mining algorithm to find patterns in the transformed data that hide sensitive information. This allows sensitive information to be protected without loss of data utility for analysis. The system architecture includes modules for data preprocessing, transformation, mining using inverse frequent itemsets, and evaluation of results.
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.
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.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
Privacy is an important issue in data mining and knowledge
discovery. In this paper, we propose to use the randomized
response techniques to conduct the data mining computation.
Specially, we present a method to build decision tree
classifiers from the disguised data. We conduct experiments
to compare the accuracy ofou r decision tree with the one
built from the original undisguised data. Our results show
that although the data are disguised, our method can still
achieve fairly high accuracy. We also show how the parameter
used in the randomized response techniques affects the
accuracy ofth e results
Keywords
Privacy, security, decision tree, data mining
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
In this paper we review on the
various privacy preserving data mining techniques like data
modification and secure multiparty computation based on the
different aspects.
Index Terms– Privacy and Security, Data Mining, Privacy
Preserving, Secure Multiparty Computation (SMC) and Data
Modification
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.
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.
Every person involved,is concerned about the leakage of private data i.e privacy of the individual's data.Today privacy of data is one of the most serious concerns which people face on an individual as well as organisational level and it has to be dealt with in an effective
manner using privacy preserving data mining.
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.
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.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
Privacy is an important issue in data mining and knowledge
discovery. In this paper, we propose to use the randomized
response techniques to conduct the data mining computation.
Specially, we present a method to build decision tree
classifiers from the disguised data. We conduct experiments
to compare the accuracy ofou r decision tree with the one
built from the original undisguised data. Our results show
that although the data are disguised, our method can still
achieve fairly high accuracy. We also show how the parameter
used in the randomized response techniques affects the
accuracy ofth e results
Keywords
Privacy, security, decision tree, data mining
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
In this paper we review on the
various privacy preserving data mining techniques like data
modification and secure multiparty computation based on the
different aspects.
Index Terms– Privacy and Security, Data Mining, Privacy
Preserving, Secure Multiparty Computation (SMC) and Data
Modification
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.
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.
Every person involved,is concerned about the leakage of private data i.e privacy of the individual's data.Today privacy of data is one of the most serious concerns which people face on an individual as well as organisational level and it has to be dealt with in an effective
manner using privacy preserving data mining.
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.
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.
A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Usin...Kato Mivule
Kato Mivule, Claude Turner, "A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge", Procedia Computer Science, Volume 20, 2013, Pages 414-419, Baltimore MD, USA
Implementation of Data Privacy and Security in an Online Student Health Recor...Kato Mivule
Kato Mivule, Stephen Otunba, Tattwamasi Tripathy, Sharad and Sharma, "Implementation of Data Privacy and Security in an Online Student Health Records System", Proceedings at the ISCA 21th Int Conf on Software Engineering and Data Engineering (SEDE-2012), Pages 143-148, Los Angeles, CA, USA
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
Applying Data Privacy Techniques on Published Data in UgandaKato Mivule
Kato Mivule, Claude Turner, "Applying Data Privacy Techniques on Published Data in Uganda", Proceedings of the 2012 International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (EEE 2012), Pages 110-115, Las Vegas, NV, USA.
Kato Mivule - Utilizing Noise Addition for Data Privacy, an OverviewKato Mivule
Kato Mivule, "Utilizing Noise Addition for Data Privacy, an Overview", Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2012), Pages 65-71, Las Vegas, NV, USA.
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Towards A Differential Privacy and Utility Preserving Machine Learning Classi...Kato Mivule
Kato Mivule, Claude Turner, Soo-Yeon Ji, "Towards A Differential Privacy and Utility Preserving Machine Learning Classifier", Procedia Computer Science (Complex Adaptive Systems), 2012, Pages 176-181, Washington DC, USA.
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
An Investigation of Data Privacy and Utility Preservation Using KNN Classific...Kato Mivule
Kato Mivule and Claude Turner, An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge, International Conference on Information and Knowledge Engineering (IKE 2013), July 22-25, Pages 203-204, Las Vegas, NV, USA
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
A Review on Privacy Preservation in Data Miningijujournal
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.
A Review on Privacy Preservation in Data Miningijujournal
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.
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.
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.
A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Usin...Kato Mivule
Kato Mivule, Claude Turner, "A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge", Procedia Computer Science, Volume 20, 2013, Pages 414-419, Baltimore MD, USA
Implementation of Data Privacy and Security in an Online Student Health Recor...Kato Mivule
Kato Mivule, Stephen Otunba, Tattwamasi Tripathy, Sharad and Sharma, "Implementation of Data Privacy and Security in an Online Student Health Records System", Proceedings at the ISCA 21th Int Conf on Software Engineering and Data Engineering (SEDE-2012), Pages 143-148, Los Angeles, CA, USA
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
Applying Data Privacy Techniques on Published Data in UgandaKato Mivule
Kato Mivule, Claude Turner, "Applying Data Privacy Techniques on Published Data in Uganda", Proceedings of the 2012 International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (EEE 2012), Pages 110-115, Las Vegas, NV, USA.
Kato Mivule - Utilizing Noise Addition for Data Privacy, an OverviewKato Mivule
Kato Mivule, "Utilizing Noise Addition for Data Privacy, an Overview", Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2012), Pages 65-71, Las Vegas, NV, USA.
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Towards A Differential Privacy and Utility Preserving Machine Learning Classi...Kato Mivule
Kato Mivule, Claude Turner, Soo-Yeon Ji, "Towards A Differential Privacy and Utility Preserving Machine Learning Classifier", Procedia Computer Science (Complex Adaptive Systems), 2012, Pages 176-181, Washington DC, USA.
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
An Investigation of Data Privacy and Utility Preservation Using KNN Classific...Kato Mivule
Kato Mivule and Claude Turner, An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge, International Conference on Information and Knowledge Engineering (IKE 2013), July 22-25, Pages 203-204, Las Vegas, NV, USA
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
A Review on Privacy Preservation in Data Miningijujournal
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.
A Review on Privacy Preservation in Data Miningijujournal
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.
A review on privacy preservation in data miningijujournal
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.
A Review on Privacy Preservation in Data Miningijujournal
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.
Data Transformation Technique for Protecting Private Information in Privacy P...acijjournal
Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. Data
Mining can be utilized in any organization that needs to find patterns or relationships in their data. A group of techniques that find relationships that have not previously been discovered. In many situations, the extracted patterns are highly private and it should not be disclosed. In order to maintain the secrecy of data,
there is in need of several techniques and algorithms for modifying the original data in order to limit the extraction of confidential patterns. There have been two types of privacy in data mining. The first type of privacy is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy is that the data is manipulated so that the mining result is not affected or minimally affected. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be
applied on data bases without violating the privacy of individuals. Many techniques for privacy preserving data mining have come up over the last decade. Some of them are statistical, cryptographic, randomization methods, k-anonymity model, l-diversity and etc. In this work, we propose a new perturbative masking technique known as data transformation technique can be used for protecting the sensitive information. An
experimental result shows that the proposed technique gives the better result compared with the existing technique.
Performance analysis of perturbation-based privacy preserving techniques: an ...IJECEIAES
Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A Comparative Study on Privacy Preserving Datamining TechniquesIJMER
Privacy protection is very important in the recent years for the reason of increasing in the
ability to store data. In particular, recent advances in the data mining field have lead to increased
concerns about privacy. Data in its original form, however, typically contains sensitive information about
individuals, and publishing such data will violate individual privacy. The current practice in data
publishing based on that what type of data can be released and use of that data. Recently, PPDM has
received immersed attention in research communities, and many approaches have been proposed for
different data publishing scenarios. In this comparative study we will systematically summarize and
evaluate different approaches for PPDM, study the challenges ,differences and requirements that
distinguish PPDM from other related problems, and propose future research directions
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS cscpconf
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
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.
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.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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.