To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Provider Aware Anonymization Algorithm for Preserving M - PrivacyIJERA Editor
In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may
use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic
algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering
techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware
anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of
anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.
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.
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.
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
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Provider Aware Anonymization Algorithm for Preserving M - PrivacyIJERA Editor
In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may
use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic
algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering
techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware
anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of
anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.
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.
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.
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
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.
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
Privacy Perserving DataBases, how they are managed, built and secured. with an introduction to main methods of Anonymization techniques, PPDB data mining, P3P and Hippocratic DBs.
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.
Protecting the private data from publishing has
become a requisite in today's world of data. This solves the
problem of divulging the sensitive data when it is mined.
Amongst the prevailing privacy models, ϵ-differential privacy
outfits one of the strongest privacy guarantees. In this paper,
we address the problem of private data publishing on vertically
partitioned data, where different attributes exist for same set of
individuals. This operation is simulated between two parties. In
specific, we present an algorithm with differentially private
data release for vertically partitioned data between two parties
in the semi-honest adversary model. First step towards
achieving this is to present a two party protocol for exponential
mechanism. By the same token, a two-party algorithm that
releases differentially private data in a secure way following
secure multiparty computation is implemented. A set of
investigational results on the real-life data indicate that the
proposed algorithm can effectively safeguard the information
for a data mining task.
Framework to Avoid Similarity Attack in Big Streaming Data IJECEIAES
The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay. In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party.
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
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
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 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
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.
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
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
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
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.
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
Privacy Perserving DataBases, how they are managed, built and secured. with an introduction to main methods of Anonymization techniques, PPDB data mining, P3P and Hippocratic DBs.
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.
Protecting the private data from publishing has
become a requisite in today's world of data. This solves the
problem of divulging the sensitive data when it is mined.
Amongst the prevailing privacy models, ϵ-differential privacy
outfits one of the strongest privacy guarantees. In this paper,
we address the problem of private data publishing on vertically
partitioned data, where different attributes exist for same set of
individuals. This operation is simulated between two parties. In
specific, we present an algorithm with differentially private
data release for vertically partitioned data between two parties
in the semi-honest adversary model. First step towards
achieving this is to present a two party protocol for exponential
mechanism. By the same token, a two-party algorithm that
releases differentially private data in a secure way following
secure multiparty computation is implemented. A set of
investigational results on the real-life data indicate that the
proposed algorithm can effectively safeguard the information
for a data mining task.
Framework to Avoid Similarity Attack in Big Streaming Data IJECEIAES
The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay. In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party.
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
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
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 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
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.
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
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
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
Utility privacy tradeoff in databases an information-theoretic approachIEEEFINALYEARPROJECTS
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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.
Cloud assisted privacy preserving and data integrity for mobile health monito...eSAT Journals
Abstract
In cloud computing system ,data is stimulated to a distantly placed cloud server. Cloud provisions the information authentically and go back to the proprietor whenever wanted. But there is no assurance that information store in the cloud is protected and not changed by cloud . In order to defeat the danger of honesty of data, the user must be able to use the help of Third party Auditor(TPA).TPA has understanding in inspection honesty of the information, that clouds users does not have, and that is difficult for the owner to check .The data in the cloud should be exact ,reliable ,available and elevated excellence.[1]This paper is to address this important problem related to the data integrity and design a cloud assisted privacy preserving mobile-health monitoring system to protect the privacy of the involved parties and their data. for that purpose we have used the algorithms:1)Tate pairing 2)Token generation 3)AES(Advanced Encryption Standards)-SHA1 and MD5.
Keywords— Data integrity,TPA,Tate pairing, Cryptography
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.
Abstract-The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining.Suppose Alice owns a k-anonymous database and needs to determine whether her database, when inserted with a tuple owned by Bob, is still k-anonymous. Also, suppose that access to the database is strictly controlled, because for example data are used for certain experiments that need to be maintained confidential. Clearly, allowing Alice to directly read the contents of the tuple breaks the privacy of Bob (e.g., a patient’s medical record); on the other hand, the confidentiality of the database managed by Alice is violated once Bob has access to the contents of the database. Thus, the problem is to check whether the database inserted with the tuple is still k-anonymous, without letting Alice and Bob know the contents of the tuple and the database, respectively. In this paper, we propose two protocols solving this problem on suppression-based and generalization-based k-anonymous and confidential databases. The protocols rely on well-known cryptographic assumptions, and we provide theoretical analyses to proof their soundness and experimental results to illustrate their efficiency.We have presented two secure protocols for privately checking whether a k-anonymous database retains its anonymity once a new tuple is being inserted to it. Since the proposed protocols ensure the updated database remains K-anonymous, the results returned from a user’s (or a medical researcher’s) query are also k-anonymous. Thus, the patient or the data provider’s privacy cannot be violated from any query. As long as the database is updated properly using the proposed protocols, the user queries under our application domain are always privacy-preserving.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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.
Privacy-Preserving Updates to Anonymous and Confidential Databaseijdmtaiir
The current trend in the application space towards
systems of loosely coupled and dynamically bound
components that enables just-in-time integration jeopardizes
the security of information that is shared between the broker,
the requester, and the provider at runtime. In particular, new
advances in data mining and knowledge discovery that allow
for the extraction of hidden knowledge in an enormous amount
of data impose new threats on the seamless integration of
information. We consider the problem of building privacy
preserving algorithms for one category of data mining
techniques, association rule mining.Suppose Alice owns a kanonymous database and needs to determine whether her
database, when inserted with a tuple owned by Bob, is still kanonymous. Also, suppose that access to the database is strictly
controlled, because for example data are used for certain
experiments that need to be maintained confidential. Clearly,
allowing Alice to directly read the contents of the tuple breaks
the privacy of Bob (e.g., a patient’s medical record); on the
other hand, the confidentiality of the database managed by
Alice is violated once Bob has access to the contents of the
database. Thus, the problem is to check whether the database
inserted with the tuple is still k-anonymous, without letting
Alice and Bob know the contents of the tuple and the database,
respectively. In this paper, we propose two protocols solving
this problem on suppression-based and generalization-based kanonymous and confidential databases. The protocols rely on
well-known cryptographic assumptions, and we provide
theoretical analyses to proof their soundness and experimental
results to illustrate their efficiency.We have presented two
secure protocols for privately checking whether a kanonymous database retains its anonymity once a new tuple is
being inserted to it. Since the proposed protocols ensure the
updated database remains K-anonymous, the results returned
from a user’s (or a medical researcher’s) query are also kanonymous. Thus, the patient or the data provider’s privacy
cannot be violated from any query. As long as the database is
updated properly using the proposed protocols, the user queries
under our application domain are always privacy-preserving
Similar to IEEE 2014 JAVA DATA MINING PROJECTS M privacy for collaborative data publishing (20)
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
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.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
IEEE 2014 JAVA DATA MINING PROJECTS M privacy for collaborative data publishing
1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
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Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
m-Privacy for Collaborative Data Publishing
Abstract:
we consider the collaborative data publishing problem for anonymizing horizontally
partitioned data at multiple data providers. We consider a new type of “insider attack” by
colluding data providers who may use their own data records (a subset of the overall data) in
addition to the external background knowledge to infer the data records contributed by other data
providers. The paper addresses this new threat and makes several contributions. First, we
introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given
privacy constraint against any group of up to m colluding data providers. Second, we present
heuristic algorithms exploiting the equivalence group monotonicity of pri-vacy constraints and
adaptive ordering techniques for efficiently checking m-privacy given a set of records. Finally,
we present a data provider-aware anonymization algorithm with adaptive m-privacy checking
strategies to ensure high utility and m-privacy of anonymized data with efficiency. Experiments
on real-life datasets suggest that our approach achieves better or comparable utility and
efficiency than existing and baseline algorithms while providing m-privacy guarantee.
3. Existing System:
We assume the data providers are semi-honest , commonly used in distributed
computation setting. They can attempt to infer additional information about data coming from
other providers by analyzing the data received during the anonymization. A data recipient, e.g.
P0, could be an attacker and attempts to infer additional information about the records using the
published data (T∗) and some background knowl- edge (BK) such as publicly available external
data.
Proposed System:
We consider the collaborative data pub-lishing setting (Figure 1B) with
horizontally partitioned data across multiple data providers, each contributing a subset of records
Ti. As a special case, a data provider could be the data owner itself who is contributing its own
records. This is a very common scenario in social networking and recommendation systems. Our
4. goal is to publish an anonymized view of the integrated data such that a data recipient including
the data providers will not be able to compromise the privacy of the individual records provided
by other parties.
Modules :
1. Patient Registration
2. Attacks by External Data Recipient Using Anonymized Data
3. Attacks by Data Providers Using Anonymized Data and Their Own Data
4. Doctor Login
5. Admin Login
Modules Description
Patient Registration:
In this module if a patient have to take treatment,he/she should register
their details like Name,Age,Disease they get affected,Email etc.These details are maintained in a
Database by the Hospital management.Only Doctors can see all their details.Patient can only see
his own record.
BASED ON THIS PAPER:
When the data are distributed among multiple data providers or data owners, two main settings
are used for anonymization . One approach is for each provider to anonymize the data
independently (anonymize-and-aggregate,Figure 1A), which results in potential loss of
5. integrated data utility. A more desirable approach is collaborative data publishing which
anonymizes data from all
providers as if they would come from one source (aggregate-and-anonymize, Figure 1B), using
either a trusted third-party(TTP) or Secure Multi-party Computation (SMC) protocols to do
computations .
Attacks by External Data Recipient Using Anonymized Data.:
A data recipient, e.g. P0, could be an attacker and attempts to infer additional information about
the records using the published data (T∗) and some background knowl- edge (BK) such as
publicly available external data.
Attacks by Data Providers Using Anonymized Data and Their Own Data:
Each data provider, such as P1 in Figure 1,can also use anonymized data T∗ and his own data
(T1) to infer additional information about other records. Compared to the attack by the external
recipient in the first attack scenario, each provider has additional data knowledge of their own
records, which can help with the attack. This issue can be further worsened when multiple data
providers collude with each other.
6. FIGURE 1
FIGURE:2
Doctor Login:
In this module Doctor can see all the patients details and will get the background
knowledge(BK),by the chance he will see horizontally partitioned data of distributed data base of
the group of hospitals and can see how many patients are affected without knowing of individual
records of the patients and sensitive information about the individuals.
Admin Login:
7. In this module Admin acts as Trusted Third Party(TTP).He can see all individual
records and their sensitive information among the overall hospital distributed data
base.Anonymation can be done by this people.He/She collected informations from various
hospitals and grouped into each other and make them as an anonymised data.
System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 GHz
RAM - 256 MB (min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
8. S/W System Configuration:-
Operating System :Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : HTML, Java, Jsp
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : My sql
Database Connectivity : JDBC.
Conclusion:
In this paper, we considered a new type of potential at-tackers in collaborative data
publishing – a coalition of data providers, called m-adversary. To prevent privacy disclosure by
any m-adversary we showed that guaranteeing m-privacy is enough. We presented heuristic
algorithms exploiting equiv-alence group monotonicity of privacy constraints and adaptive
ordering techniques for efficiently checking m-privacy. We introduced also a provider-aware
anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and
9. m-privacy of anonymized data. Our experiments confirmed that our approach achieves better or
comparable utility than existing algorithms while ensuring m-privacy efficiently. There are many
remaining research questions. Defining a proper privacy fitness score for different privacy
constraints is one of them. It also remains a question to address and model the data knowledge of
data providers when data are distributed in a vertical or ad-hoc fashion. It would be also
interesting to verify if our methods can be adapted to other kinds of data such as set-valued data.
10. m-privacy of anonymized data. Our experiments confirmed that our approach achieves better or
comparable utility than existing algorithms while ensuring m-privacy efficiently. There are many
remaining research questions. Defining a proper privacy fitness score for different privacy
constraints is one of them. It also remains a question to address and model the data knowledge of
data providers when data are distributed in a vertical or ad-hoc fashion. It would be also
interesting to verify if our methods can be adapted to other kinds of data such as set-valued data.