The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform
Dimche Kostadinov - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
arXiv: Information Theory, 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.
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EUSIPCO - Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, “encryption” (ambiguization) and “decryption” (purification) as well as storage complexity of the codebooks.
Behrooz Razeghi - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
arXiv: Information Theory, 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.
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EUSIPCO - Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, “encryption” (ambiguization) and “decryption” (purification) as well as storage complexity of the codebooks.
Slava Voloshynovskiy - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
arXiv: Information Theory, 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.
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EUSIPCO - Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, “encryption” (ambiguization) and “decryption” (purification) as well as storage complexity of the codebooks.
Sohrab Ferdowsi - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
arXiv: Information Theory, 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.
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EUSIPCO - Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Behrooz Razeghi, Slava Voloshynovskiy, Sohrab Ferdowsi, Dimche KostadinovAbstract:We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the Authorization Level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, “encryption” (ambiguization) and “decryption” (purification) as well as storage complexity of the codebooks.
Jinli Cao - One of the best experts on this subject based on the ideXlab platform.
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ADC - Formal authorisation allocation approaches for permission-role assignments using relational algebra operations
2003Co-Authors: Hua Wang, Yanchun Zhang, Jinli CaoAbstract:In this paper, we develop formal Authorization allocation algorithms for permission-role assignments. The formal approaches are based on relational structure, relational algebra and operations. The process of permission-role assignments is an important issue in role-based access control (RBAC) as it may modify the Authorization Level or imply high-Level confidential information to be derived when roles are changed and request different permissions. There are two types of problems that may arise in permission-role assignments. One is related to Authorization granting process. Conflicting permissions may be granted to a role, and as a result, users with the role may have or derive a high Level of authority. Another is related to Authorization revocation. When permission is revoked from a role, the role may still have the permission from other roles.To solve the problems, this paper presents an Authorization granting algorithm, and weak revocation and strong revocation algorithms that are based on relational algebra operations. The algorithms can be used to check conflicts and therefore to help allocate permissions without compromising the security in RBAC. We describe how to use the new algorithms with an anonymity scalable payment scheme. Finally, comparisons with other related work are discussed.
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formal authorisation allocation approaches for permission role assignments using relational algebra operations
Australasian Database Conference, 2003Co-Authors: Hua Wang, Yanchun Zhang, Jinli CaoAbstract:In this paper, we develop formal Authorization allocation algorithms for permission-role assignments. The formal approaches are based on relational structure, relational algebra and operations. The process of permission-role assignments is an important issue in role-based access control (RBAC) as it may modify the Authorization Level or imply high-Level confidential information to be derived when roles are changed and request different permissions. There are two types of problems that may arise in permission-role assignments. One is related to Authorization granting process. Conflicting permissions may be granted to a role, and as a result, users with the role may have or derive a high Level of authority. Another is related to Authorization revocation. When permission is revoked from a role, the role may still have the permission from other roles.To solve the problems, this paper presents an Authorization granting algorithm, and weak revocation and strong revocation algorithms that are based on relational algebra operations. The algorithms can be used to check conflicts and therefore to help allocate permissions without compromising the security in RBAC. We describe how to use the new algorithms with an anonymity scalable payment scheme. Finally, comparisons with other related work are discussed.
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Formal Authorization approaches for permission-role assignments using relational algebra operations
2003Co-Authors: Hua Wang, Yanchun Zhang, Jinli CaoAbstract:In this paper, we develop formal Authorization allocation algorithms for permission-role assignments. The formal approaches are based on relational structure, and relational algebra and operations. The process of permission-role assignments is an important issue in role-based access control (RBAC) as it may modify the Authorization Level or imply high-Level confidential information to be derived when roles are changed and request different permissions. There are two types of problems that may arise in permission-role assignments. One is related to Authorization granting process. Conflicting permissions may be granted to a role, and as a result, users with the role may have or derive a high Level of authority. Another is related to Authorization revocation. When a permission is revoked from a role, the role may still have the permission from other roles. To solve the problems, this paper presents an Authorization granting algorithm, and weak revocation and strong revocation algorithms that are based on relational algebra operations. The algorithms can be used to check conflicts and therefore to help allocate permissions without compromising the security in RBAC. We describe how to use the new algorithms with an anonymity scalable payment scheme. Finally, comparisons with other related work are discussed.
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WISE - Formal Authorization allocation approaches for role-based access control based on relational algebra operations
Proceedings of the Third International Conference on Web Information Systems Engineering 2002. WISE 2002., 1Co-Authors: Hua Wang, Jinli Cao, Yanchun ZhangAbstract:We develop formal Authorization allocation algorithms for role-based access control (RBAC). The formal approaches are based on relational structure, and relational algebra and operations. The process of user-role assignments is an important issue in RBAC because it may modify the Authorization Level or imply high-Level confidential information to be derived while users change positions and request different roles. There are two types of problems which may arise in user-role assignment. One is related to the Authorization granting process. When a role is granted to a user this role may conflict with other roles of the user or together with this role; the user may have or derive a high Level of authority. Another is related to Authorization revocation. When a role is revoked from a user, the user may still have the role from other roles. To solve these problems, this paper presents an Authorization granting algorithm, and weak revocation and strong revocation algorithms that are based on relational algebra. The algorithms can be used to check conflicts and therefore to help allocate roles without compromising the security in RBAC. We describe how to use the new algorithms with an anonymity scalable payment scheme. Finally, comparisons with other related work are discussed.
