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.
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.
Enabling dynamic data and indirect mutual trust for cloud computing storage s...JPINFOTECH JAYAPRAKASH
This document proposes a cloud storage scheme that allows data owners to outsource sensitive data to cloud service providers while maintaining control over the data and establishing trust between the two parties. The proposed scheme provides four key features: 1) it enables dynamic operations like modifying, inserting, deleting, and appending outsourced data blocks, 2) it ensures authorized users always receive the latest data version, 3) it establishes indirect mutual trust between the owner and cloud provider, and 4) it supports granting and revoking user access permissions. The scheme is evaluated through theoretical analysis and a prototype implementation on Amazon cloud platform.
Enabling dynamic data and indirect mutual trust for cloud computing storage s...JPINFOTECH JAYAPRAKASH
This document proposes a cloud storage scheme that allows data owners to outsource sensitive data to cloud service providers while maintaining control over the data and establishing trust between the two parties. The proposed scheme enables dynamic operations on outsourced data like modification, insertion, deletion and appending. It also ensures authorized users always receive the latest version of data and identifies misbehaving parties to address trust issues. The scheme supports access control management and its advantages include dynamic block-level operations, newness of data, indirect mutual trust and access control enforcement.
This document proposes a hybrid cloud approach for secure authorized data deduplication. It discusses how traditional deduplication systems only check for duplicate data, but do not consider user privileges. The proposed system considers both the data and user privileges during duplicate checks. It presents new deduplication constructions that support authorized duplicate checks in a hybrid cloud. The system uses convergent encryption to encrypt data before outsourcing. A security analysis shows the scheme protects data confidentiality based on the proposed security model. A prototype was implemented and tested, with results showing minimal overhead.
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.
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.
Enabling dynamic data and indirect mutual trust for cloud computing storage s...JPINFOTECH JAYAPRAKASH
This document proposes a cloud storage scheme that allows data owners to outsource sensitive data to cloud service providers while maintaining control over the data and establishing trust between the two parties. The proposed scheme provides four key features: 1) it enables dynamic operations like modifying, inserting, deleting, and appending outsourced data blocks, 2) it ensures authorized users always receive the latest data version, 3) it establishes indirect mutual trust between the owner and cloud provider, and 4) it supports granting and revoking user access permissions. The scheme is evaluated through theoretical analysis and a prototype implementation on Amazon cloud platform.
Enabling dynamic data and indirect mutual trust for cloud computing storage s...JPINFOTECH JAYAPRAKASH
This document proposes a cloud storage scheme that allows data owners to outsource sensitive data to cloud service providers while maintaining control over the data and establishing trust between the two parties. The proposed scheme enables dynamic operations on outsourced data like modification, insertion, deletion and appending. It also ensures authorized users always receive the latest version of data and identifies misbehaving parties to address trust issues. The scheme supports access control management and its advantages include dynamic block-level operations, newness of data, indirect mutual trust and access control enforcement.
This document proposes a hybrid cloud approach for secure authorized data deduplication. It discusses how traditional deduplication systems only check for duplicate data, but do not consider user privileges. The proposed system considers both the data and user privileges during duplicate checks. It presents new deduplication constructions that support authorized duplicate checks in a hybrid cloud. The system uses convergent encryption to encrypt data before outsourcing. A security analysis shows the scheme protects data confidentiality based on the proposed security model. A prototype was implemented and tested, with results showing minimal overhead.
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.
Privacy Preserved Distributed Data Sharing with Load Balancing SchemeEditor IJMTER
Data sharing services are provided under the Peer to Peer (P2P) environment. Federated
database technology is used to manage locally stored data with a federated DBMS and provide unified
data access. Information brokering systems (IBSs) are used to connect large-scale loosely federated data
sources via a brokering overlay. Information brokers redirect the client queries to the requested data
servers. Privacy preserving methods are used to protect the data location and data consumer. Brokers are
trusted to adopt server-side access control for data confidentiality. Query and access control rules are
maintained with shared data details under metadata. A Semantic-aware index mechanism is applied to
route the queries based on their content and allow users to submit queries without data or server
information.
Distributed data sharing is managed with Privacy Preserved Information Brokering (PPIB)
scheme. Attribute-correlation attack and inference attacks are handled by the PPIB. PPIB overlay
infrastructure consisting of two types of brokering components, brokers and coordinators. The brokers
acts as mix anonymizer are responsible for user authentication and query forwarding. The coordinators
concatenated in a tree structure, enforce access control and query routing based on the automata.
Automata segmentation and query segment encryption schemes are used in the Privacy-preserving
Query Brokering (QBroker). Automaton segmentation scheme is used to logically divide the global
automaton into multiple independent segments. The query segment encryption scheme consists of the
preencryption and postencryption modules.
The PPIB scheme is enhanced to support dynamic site distribution and load balancing
mechanism. Peer workloads and trust level of each peer are integrated with the site distribution process.
The PPIB is improved to adopt self reconfigurable mechanism. Automated decision support system for
administrators is included in the PPIB.
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
Oruta proposes the first privacy-preserving mechanism for public auditing of shared data stored in the cloud. It exploits ring signatures to compute verification information needed to audit integrity without revealing signer identity. The third party auditor can verify integrity of shared data without retrieving the entire file, while keeping private which user signed each block. Existing methods do not consider privacy for shared data or dynamic groups. Oruta aims to efficiently audit integrity for static groups while preserving identity privacy.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Oruta privacy preserving public auditing for shared data in the cloudNexgen Technology
Ecruitment Solutions (ECS) is one of the leading Delhi based Software Development & HR Consulting Firm, which is assessed at the level of ISO 9001:2008 standard. ECS offers an awesome project and product based solutions to many customers around the globe.
In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
Free Net is a Distributed Anonymous Information Storage and Retrieval System. Which provides an effective means of anonymous information storage and retrieval.
Privacy preserving multi-keyword ranked search over encrypted cloud dataIGEEKS TECHNOLOGIES
This document proposes a system called privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE). Existing searchable encryption systems only support single-keyword or boolean keyword search without result ranking. The proposed MRSE system allows a user to search for multiple keywords and returns documents ranked by relevance. It establishes privacy requirements and uses an efficient "coordinate matching" semantic to quantify document similarity based on keyword matches. The system architecture includes modules for data owners to encrypt and upload files, for users to search and download encrypted files, and for ranking search results.
Secure erasure code based cloud storage system with secure data forwardingPriyank Rupera
The document describes a presentation on implementing threshold proxy re-encryption and decentralized erasure code for distributed storage systems. The proposed system uses threshold proxy re-encryption and decentralized erasure coding to improve security and reliability compared to traditional general encryption schemes. Key components include storage servers, key servers, and flexible adjustment of parameters between servers. Diagrams are included showing the system architecture, workflow, use cases and sequences.
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
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.
Enhancing access privacy of range retrievals over b+treesMigrant Systems
The document proposes a new index structure called PB+tree to enhance privacy for range queries over encrypted B+trees. It first shows that an adversary can infer the structure of an encrypted B+tree and query ranges by observing I/O patterns of range queries. PB+tree aims to conceal the ordering of leaf nodes by grouping nodes into buckets and using homomorphic encryption to obscure which exact nodes are retrieved. It balances privacy with computational overhead. Experiments show PB+tree effectively impairs the adversary's ability to deduce the B+tree structure and query ranges.
The document proposes a system for multi-keyword ranked search over encrypted cloud data while preserving privacy. It addresses limitations in previous systems that allowed single keyword search or did not consider privacy. The proposed system uses asymmetric key encryption, a block-max index, and dynamic key generation to allow efficient retrieval of relevant encrypted data from the cloud without security breaches. It involves three parts: (1) a server that encrypts and stores data in the cloud and sends decryption keys; (2) a cloud server that handles search requests, ranks results, and responds; and (3) users that request data from the cloud server.
Survey on Privacy- Preserving Multi keyword Ranked Search over Encrypted Clou...Editor IJMTER
The advent of cloud computing, data owners are motivated to outsource their complex
data management systems from local sites to commercial public cloud for great flexibility and
economic savings. But for protecting data privacy, sensitive data has to be encrypted before
outsourcing.Considering the large number of data users and documents in cloud, it is crucial for
the search service to allow multi-keyword query and provide result similarity ranking to meet the
effective data retrieval need. Related works on searchable encryption focus on single keyword
search or Boolean keyword search, and rarely differentiate the search results. We first propose a
basic MRSE scheme using secure inner product computation, and then significantly improve it to
meet different privacy requirements in two levels of threat models. The Incremental High Utility
Pattern Transaction Frequency Tree (IHUPTF-Tree) is designed according to the transaction
frequency (descending order) of items to obtain a compact tree.
By using high utility pattern the items can be arranged in an efficient manner. Tree structure
is used to sort the items. Thus the items are sorted and frequent pattern is obtained. The frequent
pattern items are retrieved from the database by using hybrid tree (H-Tree) structure. So the
execution time becomes faster. Finally, the frequent pattern item that satisfies the threshold value
is displayed.
This document proposes a scheme called PRMSM that enables privacy-preserving ranked multi-keyword search on encrypted cloud data from multiple data owners. It constructs a secure search protocol that allows cloud servers to perform searches without knowing the actual data or trapdoors. It also proposes a novel function to preserve the privacy of relevance scores between keywords and files during ranking. The scheme supports dynamic key generation, user authentication, and efficient user revocation to enhance security. Experiments show the efficacy and efficiency of PRMSM.
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 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].
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
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
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.
Privacy Preserved Distributed Data Sharing with Load Balancing SchemeEditor IJMTER
Data sharing services are provided under the Peer to Peer (P2P) environment. Federated
database technology is used to manage locally stored data with a federated DBMS and provide unified
data access. Information brokering systems (IBSs) are used to connect large-scale loosely federated data
sources via a brokering overlay. Information brokers redirect the client queries to the requested data
servers. Privacy preserving methods are used to protect the data location and data consumer. Brokers are
trusted to adopt server-side access control for data confidentiality. Query and access control rules are
maintained with shared data details under metadata. A Semantic-aware index mechanism is applied to
route the queries based on their content and allow users to submit queries without data or server
information.
Distributed data sharing is managed with Privacy Preserved Information Brokering (PPIB)
scheme. Attribute-correlation attack and inference attacks are handled by the PPIB. PPIB overlay
infrastructure consisting of two types of brokering components, brokers and coordinators. The brokers
acts as mix anonymizer are responsible for user authentication and query forwarding. The coordinators
concatenated in a tree structure, enforce access control and query routing based on the automata.
Automata segmentation and query segment encryption schemes are used in the Privacy-preserving
Query Brokering (QBroker). Automaton segmentation scheme is used to logically divide the global
automaton into multiple independent segments. The query segment encryption scheme consists of the
preencryption and postencryption modules.
The PPIB scheme is enhanced to support dynamic site distribution and load balancing
mechanism. Peer workloads and trust level of each peer are integrated with the site distribution process.
The PPIB is improved to adopt self reconfigurable mechanism. Automated decision support system for
administrators is included in the PPIB.
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
Oruta proposes the first privacy-preserving mechanism for public auditing of shared data stored in the cloud. It exploits ring signatures to compute verification information needed to audit integrity without revealing signer identity. The third party auditor can verify integrity of shared data without retrieving the entire file, while keeping private which user signed each block. Existing methods do not consider privacy for shared data or dynamic groups. Oruta aims to efficiently audit integrity for static groups while preserving identity privacy.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Oruta privacy preserving public auditing for shared data in the cloudNexgen Technology
Ecruitment Solutions (ECS) is one of the leading Delhi based Software Development & HR Consulting Firm, which is assessed at the level of ISO 9001:2008 standard. ECS offers an awesome project and product based solutions to many customers around the globe.
In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
Free Net is a Distributed Anonymous Information Storage and Retrieval System. Which provides an effective means of anonymous information storage and retrieval.
Privacy preserving multi-keyword ranked search over encrypted cloud dataIGEEKS TECHNOLOGIES
This document proposes a system called privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE). Existing searchable encryption systems only support single-keyword or boolean keyword search without result ranking. The proposed MRSE system allows a user to search for multiple keywords and returns documents ranked by relevance. It establishes privacy requirements and uses an efficient "coordinate matching" semantic to quantify document similarity based on keyword matches. The system architecture includes modules for data owners to encrypt and upload files, for users to search and download encrypted files, and for ranking search results.
Secure erasure code based cloud storage system with secure data forwardingPriyank Rupera
The document describes a presentation on implementing threshold proxy re-encryption and decentralized erasure code for distributed storage systems. The proposed system uses threshold proxy re-encryption and decentralized erasure coding to improve security and reliability compared to traditional general encryption schemes. Key components include storage servers, key servers, and flexible adjustment of parameters between servers. Diagrams are included showing the system architecture, workflow, use cases and sequences.
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
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.
Enhancing access privacy of range retrievals over b+treesMigrant Systems
The document proposes a new index structure called PB+tree to enhance privacy for range queries over encrypted B+trees. It first shows that an adversary can infer the structure of an encrypted B+tree and query ranges by observing I/O patterns of range queries. PB+tree aims to conceal the ordering of leaf nodes by grouping nodes into buckets and using homomorphic encryption to obscure which exact nodes are retrieved. It balances privacy with computational overhead. Experiments show PB+tree effectively impairs the adversary's ability to deduce the B+tree structure and query ranges.
The document proposes a system for multi-keyword ranked search over encrypted cloud data while preserving privacy. It addresses limitations in previous systems that allowed single keyword search or did not consider privacy. The proposed system uses asymmetric key encryption, a block-max index, and dynamic key generation to allow efficient retrieval of relevant encrypted data from the cloud without security breaches. It involves three parts: (1) a server that encrypts and stores data in the cloud and sends decryption keys; (2) a cloud server that handles search requests, ranks results, and responds; and (3) users that request data from the cloud server.
Survey on Privacy- Preserving Multi keyword Ranked Search over Encrypted Clou...Editor IJMTER
The advent of cloud computing, data owners are motivated to outsource their complex
data management systems from local sites to commercial public cloud for great flexibility and
economic savings. But for protecting data privacy, sensitive data has to be encrypted before
outsourcing.Considering the large number of data users and documents in cloud, it is crucial for
the search service to allow multi-keyword query and provide result similarity ranking to meet the
effective data retrieval need. Related works on searchable encryption focus on single keyword
search or Boolean keyword search, and rarely differentiate the search results. We first propose a
basic MRSE scheme using secure inner product computation, and then significantly improve it to
meet different privacy requirements in two levels of threat models. The Incremental High Utility
Pattern Transaction Frequency Tree (IHUPTF-Tree) is designed according to the transaction
frequency (descending order) of items to obtain a compact tree.
By using high utility pattern the items can be arranged in an efficient manner. Tree structure
is used to sort the items. Thus the items are sorted and frequent pattern is obtained. The frequent
pattern items are retrieved from the database by using hybrid tree (H-Tree) structure. So the
execution time becomes faster. Finally, the frequent pattern item that satisfies the threshold value
is displayed.
This document proposes a scheme called PRMSM that enables privacy-preserving ranked multi-keyword search on encrypted cloud data from multiple data owners. It constructs a secure search protocol that allows cloud servers to perform searches without knowing the actual data or trapdoors. It also proposes a novel function to preserve the privacy of relevance scores between keywords and files during ranking. The scheme supports dynamic key generation, user authentication, and efficient user revocation to enhance security. Experiments show the efficacy and efficiency of PRMSM.
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 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].
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
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
This document discusses frequent pattern mining algorithms. It describes the Apriori, AprioriTid, and FP-Growth algorithms. The Apriori algorithm uses candidate generation and database scanning to find frequent itemsets. AprioriTid tracks transaction IDs to reduce scans. FP-Growth avoids candidate generation and multiple scans by building a frequent-pattern tree. It finds frequent patterns by mining the tree.
The document discusses the Apriori algorithm and modifications using hashing and graph-based approaches for mining association rules from transactional datasets. The Apriori algorithm uses multiple passes over the data to count support for candidate itemsets and prune unpromising candidates. Hashing maps itemsets to integers for efficient counting of support. The graph-based approach builds a tree structure linking frequent itemsets. Both modifications aim to improve efficiency over the original Apriori algorithm. The document also notes challenges in designing perfect hash functions for this application.
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Apriori algorithm is one of the best algorithm in Data Mining field that used to find frequent item-sets. The apriori property tells us that all non-empty subsets of a frequent itemset must also be frequent.
This algorithm is proposed by R. Agrawal and R. Srikant
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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 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.
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
This document discusses data mining techniques, including the data mining process and common techniques like association rule mining. It describes the data mining process as involving data gathering, preparation, mining the data using algorithms, and analyzing and interpreting the results. Association rule mining is explained in detail, including how it can be used to identify relationships between frequently purchased products. Methods for mining multilevel and multidimensional association rules are also summarized.
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.
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.
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.
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.
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.
NEW ALGORITHM FOR SENSITIVE RULE HIDING USING DATA DISTORTION TECHNIQUEcscpconf
Data mining is the process of extracting hidden patterns of data. Association rule mining is an
important data mining task that finds interesting association among a large set of data item. It
may disclose pattern and various kinds of sensitive information. Such information may be
protected against unauthorized access. Association rule hiding is one of the techniques of
privacy preserving data mining to protect the association rules generated by association rule
mining. This paper adopts data distortion technique for hiding sensitive association rules.
Algorithms based on this technique either hide a specific rule using data alteration technique or
hide the rules depending on the sensitivity of the items to be hidden. In the proposed technique,
positions of sensitive items are altered while maintaining the support. The proposed technique
uses the idea of representative rules to prune the rules first and then hides the sensitive rules.
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.
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.
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.
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.
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.
Improving Cloud Security Using Data MiningIOSR Journals
Cloud computing is the use of computing resources (hardware and software) that are delivered as a
service over a network (typically the Internet). It does offer great level of flexibility but this advantage comes
with a drawback. With increase in sharing of data over web there is an increase in possibility of data being
subjected to malicious attacks. Attacker/Provider can extract sensitive information by analyzing the client data
over a long period of time. Hence the privacy and security of the user’s data is compromised. In this paper we
propose an efficient distributed architecture to mitigate the risks.
This document describes a proposed intelligent supermarket system that uses a distributed implementation of the Apriori algorithm (called Dipriori) to analyze transaction data from multiple supermarket locations and identify frequent item sets and association rules. The system would have client machines at each location running Apriori locally on their transaction data and sending the frequent item sets to a central global server. The server would calculate average support counts and identify globally frequent item sets. Association rules would then be generated and used to provide insights into customer purchasing behaviors and inform targeted marketing strategies. The authors implemented this approach and found it helped improve the time efficiency of analyzing large, distributed transaction datasets.
This document summarizes a research paper that proposes using a genetic algorithm to generate high-quality association rules for measuring data quality. The genetic algorithm evaluates rules based on four metrics: confidence, completeness, comprehensibility, and interestingness. It aims to discover high-level prediction rules that perform better than traditional greedy rule induction algorithms at handling attribute interactions. The genetic algorithm represents rules as chromosomes and uses the four metrics as an objective fitness function to evaluate the quality of each rule.
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Secure mining of association rules in horizontally distributed databases
1. 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.
Disadvantage:
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2. 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.
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 perform 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
3. 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. 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.
4. 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