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
Secure mining of association rules in horizontally distributed databasesIEEEFINALYEARPROJECTS
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
Secure mining of association rules in horizontally distributed databasesPapitha Velumani
The document proposes a new protocol for securely mining association rules from horizontally distributed databases. It improves on the previous leading protocol from Kantarcioglu and Clifton by offering enhanced privacy, simplicity, and efficiency. The main innovation is a novel secure multi-party algorithm for computing the union of private subsets held by different players, without revealing additional information. This protocol leaks less excess information than the previous approach and only to a few possible coalitions rather than individual players. It also solves the problem of private set inclusion testing in a secure manner.
Enforcing secure and privacy preserving information brokering in distributed ...IEEEFINALYEARPROJECTS
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
Enforcing secure and privacy preserving information brokering in distributed ...JPINFOTECH JAYAPRAKASH
This document proposes a system called Privacy Preserving Information Brokering (PPIB) to enforce secure and privacy-preserving information brokering in distributed information sharing. Existing information brokering systems only adopt server-side access control and do not protect privacy of data location and consumers. PPIB uses brokers and coordinators, with coordinators enforcing access control and routing queries using query brokering automata. It proposes automaton segmentation and query segment encryption to securely distribute routing responsibilities among coordinators while preventing privacy inferences. This is the first work to formally define privacy attacks like attribute-correlation and propose countermeasures without overhead.
Big Data Meets Privacy:De-identification Maturity Model for Benchmarking and ...Khaled El Emam
The document discusses de-identification and the De-identification Maturity Model (DMM). The DMM is a framework that evaluates an organization's maturity in de-identifying data based on their people, processes, technologies, and measurement practices. It assesses an organization across three dimensions: practice, implementation, and automation. Higher levels of maturity indicate more robust de-identification processes that better balance privacy and data utility. The document provides examples of how the DMM could be used to evaluate different organizations' de-identification practices.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
Secure mining of association rules in horizontally distributed databasesIEEEFINALYEARPROJECTS
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
Secure mining of association rules in horizontally distributed databasesPapitha Velumani
The document proposes a new protocol for securely mining association rules from horizontally distributed databases. It improves on the previous leading protocol from Kantarcioglu and Clifton by offering enhanced privacy, simplicity, and efficiency. The main innovation is a novel secure multi-party algorithm for computing the union of private subsets held by different players, without revealing additional information. This protocol leaks less excess information than the previous approach and only to a few possible coalitions rather than individual players. It also solves the problem of private set inclusion testing in a secure manner.
Enforcing secure and privacy preserving information brokering in distributed ...IEEEFINALYEARPROJECTS
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
Enforcing secure and privacy preserving information brokering in distributed ...JPINFOTECH JAYAPRAKASH
This document proposes a system called Privacy Preserving Information Brokering (PPIB) to enforce secure and privacy-preserving information brokering in distributed information sharing. Existing information brokering systems only adopt server-side access control and do not protect privacy of data location and consumers. PPIB uses brokers and coordinators, with coordinators enforcing access control and routing queries using query brokering automata. It proposes automaton segmentation and query segment encryption to securely distribute routing responsibilities among coordinators while preventing privacy inferences. This is the first work to formally define privacy attacks like attribute-correlation and propose countermeasures without overhead.
Big Data Meets Privacy:De-identification Maturity Model for Benchmarking and ...Khaled El Emam
The document discusses de-identification and the De-identification Maturity Model (DMM). The DMM is a framework that evaluates an organization's maturity in de-identifying data based on their people, processes, technologies, and measurement practices. It assesses an organization across three dimensions: practice, implementation, and automation. Higher levels of maturity indicate more robust de-identification processes that better balance privacy and data utility. The document provides examples of how the DMM could be used to evaluate different organizations' de-identification practices.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
This document provides an overview of new technologies for data protection presented by Ulf Mattsson, Chief Security Strategist at Protegrity. It discusses several emerging technologies like homomorphic encryption, differential privacy, and secure multi-party computation that can be used to enable secure data sharing and analytics while preserving privacy. It also provides examples of how these technologies can be applied in domains like healthcare, financial services, and retail to derive insights from sensitive data in a privacy-preserving manner and in compliance with regulations.
Data mining and privacy preserving in data miningNeeda Multani
Data mining involves analyzing data from different perspectives to discover useful patterns and relationships not previously known. It can be used to increase profits, reduce costs, and more. Privacy preservation in data mining aims to protect individual privacy while still providing valid mining results, using techniques like cryptographic protocols to run algorithms on joined databases without revealing unnecessary information. Data mining has various applications like fraud detection, credit risk assessment, customer profiling, and more.
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
This document summarizes privacy preserving data mining techniques. It begins by explaining the need for privacy preserving techniques due to the sensitivity of individual data being mined from large databases. It then classifies privacy preserving techniques based on the data mining scenario, tasks, data distribution, data types, privacy definitions, and protection methods used. Several privacy preserving techniques are described in detail, including data modification techniques like data swapping, aggregation, suppression, and noise addition. Secure multiparty computation techniques that encrypt distributed data sets are also discussed. The document concludes by evaluating these techniques based on their versatility, disclosure risks, information loss, and computational costs.
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.
Privacy preserving computing and secure multi party computationUlf Mattsson
Ulf Mattsson is the Chief Security Strategist at Protegrity and has extensive experience in data encryption, tokenization, data privacy tools and security compliance. The document discusses several use cases for secure multi-party computation and homomorphic encryption including: sharing financial data between institutions while preserving privacy, using retail transaction data for secondary purposes like advertising while protecting privacy, and enabling internal data sharing within a bank for analytics while complying with regulations. It also provides overviews of important privacy-preserving computation techniques like homomorphic encryption, secure multi-party computation, differential privacy and the growth of the homomorphic encryption market.
What I learned at the Infosecurity ISACA North America Conference 2019Ulf Mattsson
The 2019 Infosecurity ISACA North America Expo and Conference was held in New York City’s Javits Convention Center on November 20-21. With more than 50 sessions spanning 5 tracks, this conference offered the best-in-class educational content ISACA members and certification holders depend on, plus unprecedented access to leaders in the security industry.
Join Ulf Mattsson, Head of Innovation at TokenX for a conference recap webinar on the biggest takeaways
Data protection and privacy in the world of database DevOpsRed Gate Software
We dispel the myth that database DevOps and compliance can't go hand in hand. We begin with a brief look at how extending DevOps to the database lays solid foundations for data governance and compliance.
Our guest speaker is James Boother, Sales and Marketing Director at Coeo. Coeo are Europe's most trusted analytics and data management experts, with over 10 years’ experience delivering technology strategy and support for businesses who need to get the most from their data.
In a climate where data breaches are all too frequent, and more and more data lives in the cloud, the challenge of protecting your data can seem daunting.
James addresses the implications of the forthcoming GDPR on database management, highlighting requirements you may need to address. As well as offering guidance for assessing your data estate for GDPR readiness, James shares some great tools and tips for building data protection and privacy into your DevOps processes. With the right preparation, you can be fully compliant, with your data safe, whilst maintaining fast delivery of value to your end users.
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.
In this work we highlighted some of the concepts of data privacy, techniques used in data privacy, and some techniques used in data privacy in the cloud plus some new research trends.
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.
Privacy preserving in data mining with hybrid approachNarendra Dhadhal
The document discusses privacy preserving techniques in data mining. It outlines various privacy preserving approaches like randomization, encryption, and anonymization. K-anonymization is described as an important anonymization technique that involves generalization and suppression of data to ensure each record is indistinguishable from at least k-1 other records. The document also reviews several research papers on privacy preserving data mining and discusses issues like homogeneity attacks with k-anonymization. A hybrid approach combining k-anonymization with perturbation is proposed to better protect sensitive data privacy.
This document summarizes several techniques for privacy preserving data mining. It begins by introducing the concepts of data mining and privacy preserving data mining. It then discusses specific techniques such as greedy-based transaction insertion to hide sensitive itemsets, a heuristic approach using pattern-based algorithms, and genetic algorithm-based transaction deletion. It also covers blocking-based techniques that replace sensitive values with unknown values. Overall, the document provides an overview of different privacy preserving data mining methods for modifying databases to hide sensitive information while maintaining data utility.
Enabling data dynamic and indirect mutual trust for cloud computing storage s...IEEEFINALYEARPROJECTS
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
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.
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.
Blockchain-Based Data Preservation System for Medical DataSwarup Saha
1) The document proposes a blockchain-based data preservation system (DPS) for medical data to address issues like data security, management and transfer between healthcare providers.
2) The proposed DPS uses blockchain technology to allow users to store medical data permanently in an untampered way and verify the primitiveness of the data.
3) The DPS aims to ensure data consistency, prevent tampering, forgery or deletion of data while maintaining user anonymity and encryption of medical information.
D-Eclat Association Rules on Vertically Partitioned Dynamic Data to Outsource...IRJET Journal
This document discusses privacy-preserving association rule mining on outsourced databases. It proposes a system where data owners encrypt and outsource their transactional databases to a cloud server. The cloud server then performs association rule mining using the D-Eclat algorithm on the vertically partitioned and encrypted data. The generated rules are returned to users in an encrypted format. The system aims to securely outsource the mining process while preserving data privacy. Experimental results show that vertically partitioning the data and using the D-Eclat algorithm improves time efficiency and system resource utilization compared to other approaches.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes a research paper that proposes a privacy-preserving model called BSS Homomorphic Encryption for outsourcing the mining of frequent patterns from large transactional databases. The model aims to encrypt transaction data in a way that protects both individual data items and mined patterns. It works by grouping data items, adding fake transactions, and converting the encrypted database into a matrix format to prevent guessing attacks. Experimental results on a real transactional database show the technique effectively protects privacy while remaining scalable.
JAVA 2013 IEEE DATAMINING PROJECT Secure mining of association rules in horiz...IEEEGLOBALSOFTTECHNOLOGIES
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
Secure mining of association rules in horizontally distributed databasesJPINFOTECH JAYAPRAKASH
This document proposes a new protocol for securely mining association rules from horizontally distributed databases. It summarizes an existing protocol from Kantarcioglu and Clifton, which leaks excess information. The proposed protocol uses novel secure multi-party algorithms to compute unions and test set inclusions privately. It offers enhanced privacy and is more efficient than the existing protocol in terms of communication rounds, cost, and computation.
This document provides an overview of new technologies for data protection presented by Ulf Mattsson, Chief Security Strategist at Protegrity. It discusses several emerging technologies like homomorphic encryption, differential privacy, and secure multi-party computation that can be used to enable secure data sharing and analytics while preserving privacy. It also provides examples of how these technologies can be applied in domains like healthcare, financial services, and retail to derive insights from sensitive data in a privacy-preserving manner and in compliance with regulations.
Data mining and privacy preserving in data miningNeeda Multani
Data mining involves analyzing data from different perspectives to discover useful patterns and relationships not previously known. It can be used to increase profits, reduce costs, and more. Privacy preservation in data mining aims to protect individual privacy while still providing valid mining results, using techniques like cryptographic protocols to run algorithms on joined databases without revealing unnecessary information. Data mining has various applications like fraud detection, credit risk assessment, customer profiling, and more.
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
This document summarizes privacy preserving data mining techniques. It begins by explaining the need for privacy preserving techniques due to the sensitivity of individual data being mined from large databases. It then classifies privacy preserving techniques based on the data mining scenario, tasks, data distribution, data types, privacy definitions, and protection methods used. Several privacy preserving techniques are described in detail, including data modification techniques like data swapping, aggregation, suppression, and noise addition. Secure multiparty computation techniques that encrypt distributed data sets are also discussed. The document concludes by evaluating these techniques based on their versatility, disclosure risks, information loss, and computational costs.
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.
Privacy preserving computing and secure multi party computationUlf Mattsson
Ulf Mattsson is the Chief Security Strategist at Protegrity and has extensive experience in data encryption, tokenization, data privacy tools and security compliance. The document discusses several use cases for secure multi-party computation and homomorphic encryption including: sharing financial data between institutions while preserving privacy, using retail transaction data for secondary purposes like advertising while protecting privacy, and enabling internal data sharing within a bank for analytics while complying with regulations. It also provides overviews of important privacy-preserving computation techniques like homomorphic encryption, secure multi-party computation, differential privacy and the growth of the homomorphic encryption market.
What I learned at the Infosecurity ISACA North America Conference 2019Ulf Mattsson
The 2019 Infosecurity ISACA North America Expo and Conference was held in New York City’s Javits Convention Center on November 20-21. With more than 50 sessions spanning 5 tracks, this conference offered the best-in-class educational content ISACA members and certification holders depend on, plus unprecedented access to leaders in the security industry.
Join Ulf Mattsson, Head of Innovation at TokenX for a conference recap webinar on the biggest takeaways
Data protection and privacy in the world of database DevOpsRed Gate Software
We dispel the myth that database DevOps and compliance can't go hand in hand. We begin with a brief look at how extending DevOps to the database lays solid foundations for data governance and compliance.
Our guest speaker is James Boother, Sales and Marketing Director at Coeo. Coeo are Europe's most trusted analytics and data management experts, with over 10 years’ experience delivering technology strategy and support for businesses who need to get the most from their data.
In a climate where data breaches are all too frequent, and more and more data lives in the cloud, the challenge of protecting your data can seem daunting.
James addresses the implications of the forthcoming GDPR on database management, highlighting requirements you may need to address. As well as offering guidance for assessing your data estate for GDPR readiness, James shares some great tools and tips for building data protection and privacy into your DevOps processes. With the right preparation, you can be fully compliant, with your data safe, whilst maintaining fast delivery of value to your end users.
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.
In this work we highlighted some of the concepts of data privacy, techniques used in data privacy, and some techniques used in data privacy in the cloud plus some new research trends.
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.
Privacy preserving in data mining with hybrid approachNarendra Dhadhal
The document discusses privacy preserving techniques in data mining. It outlines various privacy preserving approaches like randomization, encryption, and anonymization. K-anonymization is described as an important anonymization technique that involves generalization and suppression of data to ensure each record is indistinguishable from at least k-1 other records. The document also reviews several research papers on privacy preserving data mining and discusses issues like homogeneity attacks with k-anonymization. A hybrid approach combining k-anonymization with perturbation is proposed to better protect sensitive data privacy.
This document summarizes several techniques for privacy preserving data mining. It begins by introducing the concepts of data mining and privacy preserving data mining. It then discusses specific techniques such as greedy-based transaction insertion to hide sensitive itemsets, a heuristic approach using pattern-based algorithms, and genetic algorithm-based transaction deletion. It also covers blocking-based techniques that replace sensitive values with unknown values. Overall, the document provides an overview of different privacy preserving data mining methods for modifying databases to hide sensitive information while maintaining data utility.
Enabling data dynamic and indirect mutual trust for cloud computing storage s...IEEEFINALYEARPROJECTS
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
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.
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.
Blockchain-Based Data Preservation System for Medical DataSwarup Saha
1) The document proposes a blockchain-based data preservation system (DPS) for medical data to address issues like data security, management and transfer between healthcare providers.
2) The proposed DPS uses blockchain technology to allow users to store medical data permanently in an untampered way and verify the primitiveness of the data.
3) The DPS aims to ensure data consistency, prevent tampering, forgery or deletion of data while maintaining user anonymity and encryption of medical information.
D-Eclat Association Rules on Vertically Partitioned Dynamic Data to Outsource...IRJET Journal
This document discusses privacy-preserving association rule mining on outsourced databases. It proposes a system where data owners encrypt and outsource their transactional databases to a cloud server. The cloud server then performs association rule mining using the D-Eclat algorithm on the vertically partitioned and encrypted data. The generated rules are returned to users in an encrypted format. The system aims to securely outsource the mining process while preserving data privacy. Experimental results show that vertically partitioning the data and using the D-Eclat algorithm improves time efficiency and system resource utilization compared to other approaches.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes a research paper that proposes a privacy-preserving model called BSS Homomorphic Encryption for outsourcing the mining of frequent patterns from large transactional databases. The model aims to encrypt transaction data in a way that protects both individual data items and mined patterns. It works by grouping data items, adding fake transactions, and converting the encrypted database into a matrix format to prevent guessing attacks. Experimental results on a real transactional database show the technique effectively protects privacy while remaining scalable.
JAVA 2013 IEEE DATAMINING PROJECT Secure mining of association rules in horiz...IEEEGLOBALSOFTTECHNOLOGIES
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
Secure mining of association rules in horizontally distributed databasesJPINFOTECH JAYAPRAKASH
This document proposes a new protocol for securely mining association rules from horizontally distributed databases. It summarizes an existing protocol from Kantarcioglu and Clifton, which leaks excess information. The proposed protocol uses novel secure multi-party algorithms to compute unions and test set inclusions privately. It offers enhanced privacy and is more efficient than the existing protocol in terms of communication rounds, cost, and computation.
JPD1416 Secure Mining Of Association Rules In Horizantally Distributed Data...chennaijp
This document proposes a new protocol for securely mining association rules from horizontally distributed databases. It summarizes an existing protocol from Kantarcioglu and Clifton that leaks some private information, and proposes an improved protocol using novel secure multi-party algorithms for computing unions and testing set inclusion privately. The new protocol enhances privacy and efficiency through reduced communication rounds, cost, and computation compared to the existing approach.
Secure Mining of Association Rules in Horizontally Distributed DatabasesIJSRD
We suggest a protocol for secure mining of association rules in horizontally distributed databases. The existing primary protocol is that of Kantarcioglu and Clifton [1]. Our protocol, like theirs, is rely on the Fast Distributed Mining (FDM) algorithm of Cheungetal, which is not a secured distributed version of the Apriori algorithm. The major ingredients in our protocol are two novel safe multi-party algorithmsâ€â€one that calculates the combination of private subsets that each of the interacting players have, and another that tests the insertion of an element contained by one player in a subset contained by another. Our protocol offers enhanced privacy with respect to the protocol in [1]. In count, it is simpler and is signiï¬Âcantly more effective in terms of interaction rounds, communication charge and computational cost. Data mining techniques are used to discover patterns in huge databases of information. But sometimes these patterns can disclose susceptible information about the data holder or persons whose information are the subject of the patterns. The idea of privacy-preserving data mining is to recognize and prohibit such revelations as evident in the kinds of patterns learned using traditional data mining techniques.[5].
Efficient Data Mining Of Association Rules in Horizontally Distributed Databasesijircee
This document proposes a protocol to securely mine association rules from horizontally distributed databases in a privacy-preserving manner. The key aspects of the protocol are:
1) It uses a novel secure multi-party protocol to compute the union of private subsets held by different players, improving on prior work by avoiding commutative encryption and oblivious transfer.
2) It includes a protocol to test if an element held by one player is contained within a private subset held by another player.
3) Experimental results show the protocol has significantly lower communication and computation costs than prior work, while still protecting individual player's privacy beyond just the final mining results.
Secure mining of association rules in horizontally distributed databasesShakas Technologies
We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. which is an unsecured distributed version of the Apriori algorithm.
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.
This document outlines a proposed system for securely mining association rules from horizontally distributed databases while preserving data privacy. It discusses how existing algorithms have limitations regarding communication overhead and privacy as the number of database sites increases. The proposed system aims to monitor multiple sites to mine association rules securely without sites revealing private database contents. It designs modules for user transactions, admin analytics, privacy-preserving data mining, and distributed computation. The system would optimize pattern detection in distributed databases and generate association rules securely.
IRJET- Classification of Pattern Storage System and Analysis of Online Shoppi...IRJET Journal
This document discusses classifying patterns from online shopping data using data mining techniques. It proposes using the Apriori algorithm to mine frequent patterns from transaction data stored in a data warehouse. Patterns mined from the data warehouse using Apriori would then be stored in a pattern warehouse. This would allow users to view product details and related patterns when browsing items online. The system aims to efficiently analyze large amounts of user data to discover useful patterns for improving the online shopping experience.
“ALERT SYSTEM FOR NEW USER TO CREATE SAFE AREA USING BLOCK CHAIN”IRJET Journal
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The advent of Big Data has presented nee challenges in terms of Data Security. There is an increasing need of research
in technologies that can handle the vast volume of Data and make it secure efficiently. Current Technologies for securing data are
slow when applied to huge amounts of data. This paper discusses security aspect of Big Data.
1. The document describes a proposed system called HAPPI (HAsh-based and PiPelIned) architecture for hardware-enhanced association rule mining. HAPPI aims to solve performance bottlenecks in existing Apriori-based hardware schemes by reducing the frequency of loading the database into hardware.
2. HAPPI includes three hardware modules - a systolic array to compare candidate itemsets with database items, a trimming filter to eliminate infrequent items, and a hash table to filter unnecessary candidate itemsets.
3. The proposed HAPPI system is intended to address limitations of existing Apriori-based approaches that involve repeatedly loading large candidate itemsets and databases into hardware.
IRJET - Efficient Public Key Cryptosystem for Scalable Data Sharing in Cloud ...IRJET Journal
This document summarizes and evaluates several existing approaches for securely sharing data stored in the cloud. It discusses key aggregate cryptosystems that allow a user to generate a single aggregate key to decrypt a set of ciphertexts. It also reviews other techniques such as attribute-based encryption with proxy re-encryption, dynamic auditing services using random sampling and fragment structures, the Oruta system using ring signatures for public auditing of shared data, and privacy-preserving public auditing using message authentication codes. The document analyzes the advantages and disadvantages of each approach, such as increased key sizes with attribute-based encryption and the storage overhead of fragment structures.
Fusion of data from multiple sources is generating new information from existing data. Now users can access any information from inside or outside of the organization very easily. It helps to increase the user productivity and knowledge shared within the organization. But this leads to a new area of network security threat, “Inside Threat”. Now users can share critical information of organization to outside the organization if he/she has access to the information. The current network security tool cannot prevent the new threat. In this paper, we address this issue by “Building real time anomaly detection system based on users’ current behavior and previous behavior”.
Adaptive and Fast Predictions by Minimal Itemsets CreationIJERA Editor
This paper presents the MG-CHARM algorithm for mining association rules from transactional datasets. MG-CHARM aims to improve upon the Apriori and CHARM algorithms by mining only the minimal generators of frequent closed itemsets, rather than all frequent itemsets, resulting in faster performance. The algorithm works by first identifying frequent closed itemsets that satisfy a minimum support threshold, then mining just the minimal generators of those closed itemsets to derive association rules. An experimental evaluation shows MG-CHARM performs faster and uses less memory than Apriori and CHARM for the same datasets.
The document discusses data security and access control. It emphasizes that data security is important for both individuals and organizations to protect data stored in databases. As technology advances, data becomes more vulnerable to security breaches. Effective data security requires confidentiality, integrity, and availability. Access control systems are important to ensure data secrecy by checking user privileges and authorizations. Additional security measures like encryption can further enhance data protection. The paper focuses on access control and privacy requirements to examine how to guarantee data security.
This document summarizes a research paper on privacy protection of database access with partial shuffle. It discusses private information retrieval (PIR) which allows a user to query a database without revealing which records they accessed. Existing PIR schemes have high computation costs. The proposed scheme designs new algorithms, including a shuffle-based algorithm and hierarchy-based algorithm. The shuffle-based algorithm reduces costs by only shuffling a portion of the database records when needed. It introduces the concept of "partial shuffle" to periodically reshuffle some records while maintaining privacy.
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
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2014 IEEE JAVA DATA MINING PROJECT Secure mining of association rules in horizontally distributed databases
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Secure Mining of Association Rules in Horizontally
Distributed Databases
Abstract
We propose a protocol for secure mining of association rules in horizontally distributed
databases. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM)
algorithm which is an unsecured distributed version of the Apriori algorithm.
The main ingredients in our protocol are two novel secure multi-party algorithms
— one that computes the union of private subsets that each of the interacting players hold,
and another that tests the inclusion of an element held by one player in a subset held by
another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it
is simpler and is significantly more efficient in terms of communication rounds,
communication cost and computational cost.
Existing System
In Existing System, the problem of secure mining of association rules in
horizontally partitioned databases. In that setting, there are several sites (or players) that
hold homogeneous databases, i.e., databases that share the same schema but hold
information on different entities. The inputs are the partial databases, and the required
output is the list of association rules that hold in the unified database with support and
confidence no smaller.
2. Disadvantage:
o Less number of features in previous system.
o Difficulty to get accurate item set.
Proposed System
In Proposed System, propose an alternative protocol for the secure computation of the
union of private subsets. The proposed protocol improves upon that in terms of simplicity
and efficiency as well as privacy. In particular, our protocol does not depend on
commutative encryption and oblivious transfer (what simplifies it significantly and
contributes towards much reduced communication and computational costs). While our
solution is still not perfectly secure, it leaks excess information only to a small number
(three) of possible coalitions, unlike the protocol of that discloses information also to
some single players. In addition, we claim that the excess information that our protocol
may leak is less sensitive than the excess information leaked by the protocol.
Advantage:
1) As a rising subject, data mining is playing an increasingly important role in the
decision support activity of every walk of life.
2) Get Efficient Item set result based on the customer request.
Modules
1. User Module.
2. Admin Module.
3. Association Rule.
4. Apriori Algorithm.
3. Modules Description
User Module
In this module, privacy preserving data mining has considered two related
settings. One, in which the data owner and the data miner are two different entities, and
another, in which the data is distributed among several parties who aim to jointly perfor m
data mining on the unified corpus of data that they hold.
In the first setting, the goal is to protect the data records from the data miner.
Hence, the data owner aims at anonymizing the data prior to its release. The main
approach in this context is to apply data perturbation. He perturbed data can be used to
infer general trends in the data, without revealing original record information.
In the second setting, the goal is to perform data mining while protecting the data
records of each of the data owners from the other data owners.
Admin Module
In this module, is used to view user details. Admin to view the item set based on
the user processing details using association role with Apriori algorithm.
Association Rule:
Association rules are if/then statements that help uncover relationships between
seemingly unrelated data in a relational database or other information repository. An
example of an association rule would be "If a customer buys a dozen eggs, he is 80%
likely to also purchase milk."
Association rules are created by analyzing data for frequent if/then patterns and
using the criteria support and confidence to identify the most important relationships.
4. Support is an indication of how frequently the items appear in the database. Confidence
indicates the number of times the if/then statements have been found to be true.
Apriori Algorithm:
Apriori is designed to operate on databases containing transactions. The purpose of
the Apriori Algorithm is to find associations between different sets of data. It is sometimes
referred to as "Market Basket Analysis". Each set of data has a number of items and is
called a transaction. The output of Apriori is sets of rules that tell us how often items are
contained in sets of data.
Algorithm - Fast Distributed Mining (FDM)
The FDM algorithm proceeds as follows:
(1) Initialization
(2) Candidate Sets Generation
(3) Local Pruning
(4) Unifying the candidate item sets
(5) Computing local supports
(6) Broadcast Mining Results
SYSTEM SPECIFICATION
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
• Keyboard : 101 Keyboards.
Software Requirements:
• Operating system : Windows 7 Ultimate (32-bit)
• Front End : VS2010
• Coding Language : ASP.Net with C#
• Data Base : SQL Server 2008