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 paper addresses privacy concerns in collaborative data publishing across multiple data providers. It introduces the concept of "m-privacy" which guarantees anonymity against any group of up to m colluding data providers. It presents algorithms to efficiently check for m-privacy and an anonymization method that adaptively ensures m-privacy and high data utility. Secure multi-party computation protocols are also proposed to enable collaborative publishing with m-privacy. Experiments show the approach achieves better or comparable utility compared to existing methods while satisfying m-privacy.
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
Provider Aware Anonymization Algorithm for Preserving M - PrivacyIJERA Editor
In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may
use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic
algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering
techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware
anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of
anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.
This document discusses privacy concerns when collaboratively publishing horizontally partitioned data from multiple data providers. It introduces the concept of an "m-adversary", which is a group of up to m colluding data providers. It also introduces the notion of "m-privacy", which guarantees anonymity against such m-adversaries. The paper then presents algorithms for efficiently checking m-privacy while maximizing data utility and handling different m-adversary attack scenarios. Experiments on real datasets show the approach achieves better utility and efficiency than existing methods while providing m-privacy guarantees.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Utility privacy tradeoff in databases an information-theoretic approachIEEEFINALYEARPROJECTS
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
A Novel Approach To Answer Continuous Aggregation Queries Using Data Aggregat...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This paper addresses privacy concerns in collaborative data publishing across multiple data providers. It introduces the concept of "m-privacy" which guarantees anonymity against any group of up to m colluding data providers. It presents algorithms to efficiently check for m-privacy and an anonymization method that adaptively ensures m-privacy and high data utility. Secure multi-party computation protocols are also proposed to enable collaborative publishing with m-privacy. Experiments show the approach achieves better or comparable utility compared to existing methods while satisfying m-privacy.
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
Provider Aware Anonymization Algorithm for Preserving M - PrivacyIJERA Editor
In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may
use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic
algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering
techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware
anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of
anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.
This document discusses privacy concerns when collaboratively publishing horizontally partitioned data from multiple data providers. It introduces the concept of an "m-adversary", which is a group of up to m colluding data providers. It also introduces the notion of "m-privacy", which guarantees anonymity against such m-adversaries. The paper then presents algorithms for efficiently checking m-privacy while maximizing data utility and handling different m-adversary attack scenarios. Experiments on real datasets show the approach achieves better utility and efficiency than existing methods while providing m-privacy guarantees.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Utility privacy tradeoff in databases an information-theoretic approachIEEEFINALYEARPROJECTS
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
A Novel Approach To Answer Continuous Aggregation Queries Using Data Aggregat...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The document summarizes a study on protecting user privacy when querying encrypted databases. It first describes how an adversary can infer information about user queries by monitoring I/O activity, even with an encrypted database and B+ tree. It then proposes a PB+ tree index that conceals the order of leaf nodes to prevent the adversary from determining the exact nodes or query ranges accessed. Finally, it notes that PB+ tree balances privacy and computational overhead, and experiments show it effectively impairs the adversary's ability to learn the B+ tree structure or query ranges in different scenarios.
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.
- Data mining involves discovering novel patterns from large databases using algorithms and computers. It aims to find hidden patterns in datasets by analyzing attribute correlations.
- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
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.
This document presents a method for achieving efficient and secure semantic search over encrypted cloud data. It proposes using vector space modeling and TF-IDF weighting to support multi-keyword ranked search. It also aims to support semantic search by extending keywords with synonyms from WordNet ontology. This allows users to search by keyword meaning even if they do not know the exact keywords. The method constructs a semantic relationship library to record similarity between keywords based on co-occurrence. It evaluates using an enhanced TF-IDF algorithm to incorporate direct keyword matches, variations, and synonyms to improve search relevance.
[M3A1] Data Analysis and Interpretation Specialization Andrea Rubio
This document describes the data source and collection procedures for terrorism event data from the Global Terrorism Database (GTD). The GTD is an open-source database that includes information on over 150,000 terrorist incidents from around the world between 1970 and 2015. The data was collected in phases by different organizations and involves both automated and manual processes to identify relevant news articles, which are then reviewed and coded according to the GTD codebook. The current collection is conducted by the National Consortium for the Study of Terrorism and Responses to Terrorism.
How Data Commons are Changing the Way that Large Datasets Are Analyzed and Sh...Robert Grossman
Data commons are emerging as a solution to challenges in analyzing and sharing large biomedical datasets. A data commons co-locates data with cloud computing infrastructure and software tools to create an interoperable resource for the research community. Examples include the NCI Genomic Data Commons and the Open Commons Consortium. The open source Gen3 platform supports building disease- or project-specific data commons to facilitate open data sharing while protecting patient privacy. Developing interoperable data commons can accelerate research through increased access to data.
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.
IRJET- Transaction of Healthcare Records using BlockchainIRJET Journal
This document discusses using blockchain technology to securely store and transmit healthcare records. It describes how blockchain works, including the components like blocks and chains, and algorithms like proof of work. The paper proposes implementing a private blockchain for healthcare to allow secure sharing of patient records between doctors and patients.
data mining privacy concerns ppt presentationiWriteEssays
Data Mining and privacy Presentation
This is a sample presentation on data mining. The presetation looks at the critical Issues In Data Mining: Privacy, National Security And Personal Liberty Implications Of Data Mining
This document discusses differential privacy and how it allows private data to be shared while protecting individuals' privacy. It introduces differential privacy as a concept where statistics produced from datasets are insensitive to changes in individuals' data. It then provides examples of how differentially private techniques can be used to share summary statistics like means, histograms, and linear regression results from private datasets while adding just enough mathematical noise to the results to prevent individuals from being reidentified.
IRJET- Study Paper on: Ontology-based Privacy Data Chain Disclosure Disco...IRJET Journal
1. The document proposes an ontology-based privacy data chain disclosure discovery method for big data. It aims to prevent unauthorized disclosure of users' sensitive private data when multiple SaaS services collaborate and share data.
2. The method first describes privacy requirements using ontology and then checks if services are authorized to access private attributes to prevent unauthorized access. It also monitors authorized services to ensure privacy requirements are followed.
3. Experiments demonstrate the feasibility and correctness of the proposed method in checking for privacy disclosures among collaborating SaaS services. This helps improve service trustworthiness and provides a basis for privacy-oriented trust assessments.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
1. Data commons co-locate large biomedical datasets with cloud computing infrastructure and analysis tools to create shared resources for the research community.
2. The NCI Genomic Data Commons is an example of a data commons that makes over 2.5 petabytes of cancer genomics data available through web portals, APIs, and harmonized analysis pipelines.
3. The Gen3 platform is an open source software stack for building data commons that can interoperate through common APIs and data models to support reproducible, collaborative research across projects.
The document provides guidance on opening up organizational data through an open data program. It recommends choosing existing data sets to publish, applying an open license, making the data available and discoverable on the web through various technical means, and establishing principles like keeping it simple and engaging stakeholders. The end goal is to organize an "innovative movement" by building networks of developers and innovators around openly published data sets.
NEGOTIATION ON A NEW POLICY IN SERVICEijwscjournal
During interactions between organizations in the field of service-oriented architecture, some security requirements may change and new security policies addressed. Security requirements and capabilities of Web services are defined as security policies. The purpose of this paper is reconciliation of dynamic security policies and to explore the possibility of requirements of the new defined security policies.During the process of applying the defined dynamic policy, is checked whether the service provider can accept the new policy or not. Therefore, the compatibility between existing policies and new defined
policies are checked, and because the available algorithms for sharing between the two policies, resulted in duplication and contradictory assertion, in this paper for providing a compromise between the provided policy and the new policy, the fuzzy inference method mamdany is used . and by comparing the security level of proposed policy with the specified functionality, the negotiating procedure is done . The difference between the work done in this paper and previous works is in fuzzy calculation and conclusion for
negotiations. the advantages of thi work is that policies are defined dynamically and applied to bpel , also can be changed independently of bpel file.
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
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses incentive compatible privacy-preserving data analysis. It proposes techniques that would motivate participating parties in data analysis tasks to provide truthful private inputs. Key theorems are developed to analyze what types of distributed data analysis functionalities could be implemented in a way that parties are incentivized to provide their true private data, such as certain data analysis tasks being in the best interest of parties to tell the truth. The number of dishonest parties is assumed to be at most one less than the total number of parties engaged in the analysis.
Protecting the private data from publishing has
become a requisite in today's world of data. This solves the
problem of divulging the sensitive data when it is mined.
Amongst the prevailing privacy models, ϵ-differential privacy
outfits one of the strongest privacy guarantees. In this paper,
we address the problem of private data publishing on vertically
partitioned data, where different attributes exist for same set of
individuals. This operation is simulated between two parties. In
specific, we present an algorithm with differentially private
data release for vertically partitioned data between two parties
in the semi-honest adversary model. First step towards
achieving this is to present a two party protocol for exponential
mechanism. By the same token, a two-party algorithm that
releases differentially private data in a secure way following
secure multiparty computation is implemented. A set of
investigational results on the real-life data indicate that the
proposed algorithm can effectively safeguard the information
for a data mining task.
This document describes a system for detecting data leakage when sensitive data is distributed to third party agents. It proposes strategies for allocating data across agents to improve the ability to identify leaks. These strategies include injecting fake records to act as watermarks without modifying real data. The system models agent guilt and develops algorithms to optimize data distribution and detect leaks. It includes modules for data allocation, fake objects, leakage protection, and identifying guilty agents.
The document summarizes a study on protecting user privacy when querying encrypted databases. It first describes how an adversary can infer information about user queries by monitoring I/O activity, even with an encrypted database and B+ tree. It then proposes a PB+ tree index that conceals the order of leaf nodes to prevent the adversary from determining the exact nodes or query ranges accessed. Finally, it notes that PB+ tree balances privacy and computational overhead, and experiments show it effectively impairs the adversary's ability to learn the B+ tree structure or query ranges in different scenarios.
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.
- Data mining involves discovering novel patterns from large databases using algorithms and computers. It aims to find hidden patterns in datasets by analyzing attribute correlations.
- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
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.
This document presents a method for achieving efficient and secure semantic search over encrypted cloud data. It proposes using vector space modeling and TF-IDF weighting to support multi-keyword ranked search. It also aims to support semantic search by extending keywords with synonyms from WordNet ontology. This allows users to search by keyword meaning even if they do not know the exact keywords. The method constructs a semantic relationship library to record similarity between keywords based on co-occurrence. It evaluates using an enhanced TF-IDF algorithm to incorporate direct keyword matches, variations, and synonyms to improve search relevance.
[M3A1] Data Analysis and Interpretation Specialization Andrea Rubio
This document describes the data source and collection procedures for terrorism event data from the Global Terrorism Database (GTD). The GTD is an open-source database that includes information on over 150,000 terrorist incidents from around the world between 1970 and 2015. The data was collected in phases by different organizations and involves both automated and manual processes to identify relevant news articles, which are then reviewed and coded according to the GTD codebook. The current collection is conducted by the National Consortium for the Study of Terrorism and Responses to Terrorism.
How Data Commons are Changing the Way that Large Datasets Are Analyzed and Sh...Robert Grossman
Data commons are emerging as a solution to challenges in analyzing and sharing large biomedical datasets. A data commons co-locates data with cloud computing infrastructure and software tools to create an interoperable resource for the research community. Examples include the NCI Genomic Data Commons and the Open Commons Consortium. The open source Gen3 platform supports building disease- or project-specific data commons to facilitate open data sharing while protecting patient privacy. Developing interoperable data commons can accelerate research through increased access to data.
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.
IRJET- Transaction of Healthcare Records using BlockchainIRJET Journal
This document discusses using blockchain technology to securely store and transmit healthcare records. It describes how blockchain works, including the components like blocks and chains, and algorithms like proof of work. The paper proposes implementing a private blockchain for healthcare to allow secure sharing of patient records between doctors and patients.
data mining privacy concerns ppt presentationiWriteEssays
Data Mining and privacy Presentation
This is a sample presentation on data mining. The presetation looks at the critical Issues In Data Mining: Privacy, National Security And Personal Liberty Implications Of Data Mining
This document discusses differential privacy and how it allows private data to be shared while protecting individuals' privacy. It introduces differential privacy as a concept where statistics produced from datasets are insensitive to changes in individuals' data. It then provides examples of how differentially private techniques can be used to share summary statistics like means, histograms, and linear regression results from private datasets while adding just enough mathematical noise to the results to prevent individuals from being reidentified.
IRJET- Study Paper on: Ontology-based Privacy Data Chain Disclosure Disco...IRJET Journal
1. The document proposes an ontology-based privacy data chain disclosure discovery method for big data. It aims to prevent unauthorized disclosure of users' sensitive private data when multiple SaaS services collaborate and share data.
2. The method first describes privacy requirements using ontology and then checks if services are authorized to access private attributes to prevent unauthorized access. It also monitors authorized services to ensure privacy requirements are followed.
3. Experiments demonstrate the feasibility and correctness of the proposed method in checking for privacy disclosures among collaborating SaaS services. This helps improve service trustworthiness and provides a basis for privacy-oriented trust assessments.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
1. Data commons co-locate large biomedical datasets with cloud computing infrastructure and analysis tools to create shared resources for the research community.
2. The NCI Genomic Data Commons is an example of a data commons that makes over 2.5 petabytes of cancer genomics data available through web portals, APIs, and harmonized analysis pipelines.
3. The Gen3 platform is an open source software stack for building data commons that can interoperate through common APIs and data models to support reproducible, collaborative research across projects.
The document provides guidance on opening up organizational data through an open data program. It recommends choosing existing data sets to publish, applying an open license, making the data available and discoverable on the web through various technical means, and establishing principles like keeping it simple and engaging stakeholders. The end goal is to organize an "innovative movement" by building networks of developers and innovators around openly published data sets.
NEGOTIATION ON A NEW POLICY IN SERVICEijwscjournal
During interactions between organizations in the field of service-oriented architecture, some security requirements may change and new security policies addressed. Security requirements and capabilities of Web services are defined as security policies. The purpose of this paper is reconciliation of dynamic security policies and to explore the possibility of requirements of the new defined security policies.During the process of applying the defined dynamic policy, is checked whether the service provider can accept the new policy or not. Therefore, the compatibility between existing policies and new defined
policies are checked, and because the available algorithms for sharing between the two policies, resulted in duplication and contradictory assertion, in this paper for providing a compromise between the provided policy and the new policy, the fuzzy inference method mamdany is used . and by comparing the security level of proposed policy with the specified functionality, the negotiating procedure is done . The difference between the work done in this paper and previous works is in fuzzy calculation and conclusion for
negotiations. the advantages of thi work is that policies are defined dynamically and applied to bpel , also can be changed independently of bpel file.
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
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses incentive compatible privacy-preserving data analysis. It proposes techniques that would motivate participating parties in data analysis tasks to provide truthful private inputs. Key theorems are developed to analyze what types of distributed data analysis functionalities could be implemented in a way that parties are incentivized to provide their true private data, such as certain data analysis tasks being in the best interest of parties to tell the truth. The number of dishonest parties is assumed to be at most one less than the total number of parties engaged in the analysis.
Protecting the private data from publishing has
become a requisite in today's world of data. This solves the
problem of divulging the sensitive data when it is mined.
Amongst the prevailing privacy models, ϵ-differential privacy
outfits one of the strongest privacy guarantees. In this paper,
we address the problem of private data publishing on vertically
partitioned data, where different attributes exist for same set of
individuals. This operation is simulated between two parties. In
specific, we present an algorithm with differentially private
data release for vertically partitioned data between two parties
in the semi-honest adversary model. First step towards
achieving this is to present a two party protocol for exponential
mechanism. By the same token, a two-party algorithm that
releases differentially private data in a secure way following
secure multiparty computation is implemented. A set of
investigational results on the real-life data indicate that the
proposed algorithm can effectively safeguard the information
for a data mining task.
This document describes a system for detecting data leakage when sensitive data is distributed to third party agents. It proposes strategies for allocating data across agents to improve the ability to identify leaks. These strategies include injecting fake records to act as watermarks without modifying real data. The system models agent guilt and develops algorithms to optimize data distribution and detect leaks. It includes modules for data allocation, fake objects, leakage protection, and identifying guilty agents.
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This document summarizes a research paper that proposes a novel method for detecting data leakage. The method involves distributing data to agents while adding fake objects using watermarking. If leaked data contains fake objects, the agent that received those objects can be identified as guilty of the leakage. The paper outlines existing data leakage detection techniques, proposes a methodology using fake objects and watermarking, and models the probabilities of agents being guilty of leakage given the leaked data. The addition of fake objects allows detection of leakage without modifying real data objects.
This document proposes methods for a data distributor to detect if an agent has leaked sensitive data, without modifying the original data. It involves strategically allocating data objects among agents, including injecting fake objects, to improve the ability to identify a guilty agent if a leak occurs. Algorithms are presented and evaluated for both explicit and sample data requests from agents. The goal is to satisfy requests while maximizing the detection of leaks and identification of agents responsible.
This document proposes a new approach for preserving sensitive data privacy when clustering data. It involves adding noise to numeric attributes in the data using a fuzzy membership function, which distorts the data while maintaining the original clusters. This method is compared to other privacy preservation techniques like data swapping and noise addition. It is found to reduce processing time compared to other methods. The document outlines literature on privacy preservation techniques including data modification, cryptography, and data reconstruction methods. It then describes the proposed method of using a fuzzy membership function to add noise to sensitive attributes before clustering the data.
This document proposes a new approach for preserving sensitive data privacy when clustering data. It involves adding noise to numeric attributes in the data using a fuzzy membership function, which distorts the data while maintaining the original clusters. The fuzzy membership function uses a S-shaped curve to map original attribute values to modified values. Clustering is then performed on the distorted data. This approach aims to preserve privacy while reducing processing time compared to other privacy-preserving methods like cryptographic techniques, data swapping, and noise addition.
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The document discusses incentive compatible privacy-preserving data analysis techniques. It proposes developing key theorems to analyze what types of privacy-preserving data analysis tasks can be conducted such that providing truthful private inputs is in each party's best interest. Existing techniques cannot verify truthful inputs, but this approach aims to make truthfulness the rational choice through game theoretic analysis of tasks like association rule mining on horizontally and vertically partitioned databases.
Achieving Privacy in Publishing Search logsIOSR Journals
The document discusses algorithms for publishing search logs while preserving user privacy. It analyzes a search log using an algorithm that produces three types of outputs: query counts, a query-action graph showing query-result click counts, and a query-reformulation graph showing query suggestions clicked. The algorithm adds noise to query counts before publishing to achieve differential privacy. It aims to provide useful aggregated information for applications like search improvement while preventing re-identification of individual user data in the search log.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
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.
PUBLIC INTEGRIYT AUDITING FOR SHARED DYNAMIC DATA STORAGE UNDER ONTIME GENERA...paperpublications3
Abstract: Nowadays verifying the result of the remote computation plays a crucial role in addressing in issue of trust. The outsourced data collection comes for multiple data sources to diagnose the originator of errors by allotting each data sources a unique secrete key which requires the inner product conformation to be performed under any two parties different keys. The proposed methods outperform AISM technique to minimize the running time. The multi-key setting is given different secrete keys, multiple data sources can be upload their data streams along with their respective verifiable homomorphic tag. The AISM consist of three novel join techniques depending on the ADS availability: (i) Authenticated Indexed Sort Merge Join (AISM), which utilizes a single ADS on the join attribute, (ii) Authenticated Index Merge Join (AIM) that requires an ADS (on the join attribute) for both relations, and (iii) Authenticated Sort Merge Join (ASM), which does not rely on any ADS. The client should allow choosing any portion in the data streams for queries. The communication between the client and server is independent of input size. The inner product evaluation can be performed by any two sources and the result can be verified by using the particular tag.
Keywords: Computation of outsourcing, Data Stream, Multiple Key, Homomorphic encryption.
Title: PUBLIC INTEGRIYT AUDITING FOR SHARED DYNAMIC DATA STORAGE UNDER ONTIME GENERATED MULTIPLE KEYS
Author: C. NISHA MALAR, M. S. BONSHIA BINU
ISSN 2350-1049
International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)
Paper Publications
The document proposes a new concept called Misuseability Weight to estimate the potential harm from leaked or misused data exposed to insiders. It assigns a score representing the sensitivity level of the exposed data to predict how it could be maliciously exploited. The M-Score measure calculates this weight for tabular data by using a sensitivity function from domain experts. It aims to help organizations assess risks from insider data exposure and take appropriate prevention steps.
Framework to Avoid Similarity Attack in Big Streaming Data IJECEIAES
The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay. In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party.
This document discusses privacy-preserving data mining and cryptography. It explains that separate medical institutions may want to conduct joint research while preserving patient privacy. It also discusses how ultra-large databases hold transaction records and how privacy-preserving protocols are needed to limit information leaks during distributed computations, even from adversarial participants. Finally, it discusses how cryptography can enable functions to be computed securely in a way that preserves individual privacy and reveals only the final results of data mining computations.
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M privacy for collaborative data publishing
1. m-Privacy for Collaborative Data Publishing
Abstract:
we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data
providers. We consider a new type of “insider attack” by colluding data providers who may use their own data records (a
subset of the overall data) in addition to the external background knowledge to infer the data records contributed by
other data providers. The paper addresses this new threat and makes several contributions. First, we introduce the
notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of
up to m colluding data providers. Second, we present heuristic algorithms exploiting the equivalence group
monotonicity of pri-vacy constraints and adaptive ordering techniques for efficiently checking m-privacy given a set of
records. Finally, we present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies
to ensure high utility and m-privacy of anonymized data with efficiency. Experiments on real-life datasets suggest that
our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while providing
m-privacy guarantee.
Architecture 1:
Architecture 2:
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2. Existing System:
We assume the data providers are semi-honest , commonly used in distributed computation setting.
They can attempt to infer additional information about data coming from other providers by analyzing the data received
during the anonymization. A data recipient, e.g. P0, could be an attacker and attempts to infer additional information
about the records using the published data (T∗) and some background knowl- edge (BK) such as publicly available
external data.
Proposed System:
We consider the collaborative data pub-lishing setting (Figure 1B) with horizontally partitioned data
across multiple data providers, each contributing a subset of records Ti. As a special case, a data provider could be the
data owner itself who is contributing its own records. This is a very common scenario in social networking and
recommendation systems. Our goal is to publish an anonymized view of the integrated data such that a data recipient
including the data providers will not be able to compromise the privacy of the individual records provided by other
parties.
3. Modules :
1. Patient Registration
2. Attacks by External Data Recipient Using Anonymized Data
3. Attacks by Data Providers Using Anonymized Data and Their Own Data
4. Doctor Login
5. Admin Login
Modules Description
Patient Registration:
In this module if a patient have to take treatment,he/she should register their details like
Name,Age,Disease they get affected,Email etc.These details are maintained in a Database by the Hospital
management.Only Doctors can see all their details.Patient can only see his own record.
BASED ON THIS PAPER:
When the data are distributed among multiple data providers or data owners, two main settings are used for
anonymization . One approach is for each provider to anonymize the data independently (anonymize-and-
aggregate,Figure 1A), which results in potential loss of integrated data utility. A more desirable approach is collaborative
data publishing which anonymizes data from all
providers as if they would come from one source (aggregate-and-anonymize, Figure 1B), using either a trusted third-
party(TTP) or Secure Multi-party Computation (SMC) protocols to do computations .
Attacks by External Data Recipient Using Anonymized Data.:
A data recipient, e.g. P0, could be an attacker and attempts to infer additional information about the records using the
published data (T∗) and some background knowl- edge (BK) such as publicly available external data.
4. Attacks by Data Providers Using Anonymized Data and Their Own Data:
Each data provider, such as P1 in Figure 1,can also use anonymized data T∗ and his own data (T1) to infer additional
information about other records. Compared to the attack by the external recipient in the first attack scenario, each
provider has additional data knowledge of their own records, which can help with the attack. This issue can be further
worsened when multiple data providers collude with each other.
FIGURE 1
FIGURE:2
Doctor Login:
In this module Doctor can see all the patients details and will get the background knowledge(BK),by the
chance he will see horizontally partitioned data of distributed data base of the group of hospitals and can see how many
5. patients are affected without knowing of individual records of the patients and sensitive information about the
individuals.
Admin Login:
In this module Admin acts as Trusted Third Party(TTP).He can see all individual records and their sensitive
information among the overall hospital distributed data base.Anonymation can be done by this people.He/She collected
informations from various hospitals and grouped into each other and make them as an anonymised data.
System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 GHz
RAM - 256 MB (min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
6. S/W System Configuration:-
Operating System :Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : HTML, Java, Jsp
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : My sql
Database Connectivity : JDBC.
Conclusion:
In this paper, we considered a new type of potential at-tackers in collaborative data publishing – a coalition
of data providers, called m-adversary. To prevent privacy disclosure by any m-adversary we showed that guaranteeing
m-privacy is enough. We presented heuristic algorithms exploiting equiv-alence group monotonicity of privacy
constraints and adaptive ordering techniques for efficiently checking m-privacy. We introduced also a provider-aware
anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized
data. Our experiments confirmed that our approach achieves better or comparable utility than existing algorithms while
ensuring m-privacy efficiently. There are many remaining research questions. Defining a proper privacy fitness score for
different privacy constraints is one of them. It also remains a question to address and model the data knowledge of data
providers when data are distributed in a vertical or ad-hoc fashion. It would be also interesting to verify if our methods
can be adapted to other kinds of data such as set-valued data.