The Experts below are selected from a list of 1842 Experts worldwide ranked by ideXlab platform
S Charanyaa - One of the best experts on this subject based on the ideXlab platform.
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A Novel Activity Flow Model for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new methods to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, activity flow model and modular workflow model of the system proposed
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Developing Use Cases and State Transition Models for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new object oriented design of use cases and state transition models to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, use cases and state transition models of the system proposed
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Survey on Security on Cloud Computing by Trusted Computer Strategy
2020Co-Authors: K Deepika, Prof S Balamurugan, Naveen N Prasad, S CharanyaaAbstract:ABSTRACT: This paper reviews methods developed for anonymizing data from 2009 to 2010. Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in Disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent Attribute Disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive Attribute across equivalence class can substantially improve the privacy as against information Disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive Attribute in a microdata and also maintaining diversity
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Investigations on Evolution of Approaches Developed for Data Privacy
2020Co-Authors: R S Venkatesh, Prof S Balamurugan, P K Reejeesh, S CharanyaaAbstract:ABSTRACT: This paper reviews methods developed for anonymizing data from 1984 to 1988 . Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in Disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent Attribute Disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive Attribute across equivalence class can substantially improve the privacy as against information Disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive Attribute in a microdata and also maintaining diversity
Ian Molloy - One of the best experts on this subject based on the ideXlab platform.
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Slicing: A New Approach for Privacy Preserving Data Publishing
IEEE Transactions on Knowledge and Data Engineering, 2012Co-Authors: Tiancheng Li, Ninghui Li, Jianqing Zhang, Ian MolloyAbstract:Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership Disclosure and does not apply for data that do not have a clear separation between quasi-identifying Attributes and sensitive Attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership Disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for Attribute Disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive Attribute. Our experiments also demonstrate that slicing can be used to prevent membership Disclosure.
Nagabhushana Prabhu - One of the best experts on this subject based on the ideXlab platform.
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accuracy constrained privacy preserving access control mechanismfor relational data
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Zahid Pervaiz, Walid G Aref, Arif Ghafoor, Nagabhushana PrabhuAbstract:Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized user can still compromise the privacy of a person leading to identity Disclosure. A PPM can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity, against identity and Attribute Disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.
Prof S Balamurugan - One of the best experts on this subject based on the ideXlab platform.
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A Novel Activity Flow Model for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new methods to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, activity flow model and modular workflow model of the system proposed
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Developing Use Cases and State Transition Models for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new object oriented design of use cases and state transition models to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, use cases and state transition models of the system proposed
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Survey on Security on Cloud Computing by Trusted Computer Strategy
2020Co-Authors: K Deepika, Prof S Balamurugan, Naveen N Prasad, S CharanyaaAbstract:ABSTRACT: This paper reviews methods developed for anonymizing data from 2009 to 2010. Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in Disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent Attribute Disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive Attribute across equivalence class can substantially improve the privacy as against information Disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive Attribute in a microdata and also maintaining diversity
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Investigations on Evolution of Approaches Developed for Data Privacy
2020Co-Authors: R S Venkatesh, Prof S Balamurugan, P K Reejeesh, S CharanyaaAbstract:ABSTRACT: This paper reviews methods developed for anonymizing data from 1984 to 1988 . Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in Disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent Attribute Disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive Attribute across equivalence class can substantially improve the privacy as against information Disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive Attribute in a microdata and also maintaining diversity
V M Prabhakaran - One of the best experts on this subject based on the ideXlab platform.
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A Novel Activity Flow Model for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new methods to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, activity flow model and modular workflow model of the system proposed
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Developing Use Cases and State Transition Models for Effective Protection of Electronic Health Records (EHRs) in Cloud
2020Co-Authors: V M Prabhakaran, Prof S Balamurugan, S CharanyaaAbstract:ABSTRACT: This paper proposes new object oriented design of use cases and state transition models to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information Disclosures they are: Identity Disclosure and Attribute Disclosure. Identity Disclosure occurs when an individual's linked to a particular record in the released Attribute Disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, use cases and state transition models of the system proposed
