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Sadegh Jamshidpour - One of the best experts on this subject based on the ideXlab platform.

  • Linear Subspace cryptanalysis of harn s secret sharing based group authentication scheme
    IEEE Transactions on Information Forensics and Security, 2018
    Co-Authors: Zahra Ahmadian, Sadegh Jamshidpour
    Abstract:

    Shamir's secret sharing is used as an important underlying primitive in many other cryptographic schemes, such as group authentication and group key agreement schemes. Although Shamir secret sharing has unconditional security, it is not necessarily the case for the protocols founded on that. A common imperfect assumption in such schemes is to be satisfied of only hiding the polynomials coefficients from the adversary. In this direction, we present a new method that can be potentially used for cryptanalysis of some Shamir's secret sharing-based schemes. This method is called the Linear Subspace cryptanalysis, in which the attack problem is made equivalent to the problem of studying the belongingness of a vector to a given Linear Subspace. Using the proposed method, we analyse the Harn's group authentication protocol, which is a remarkable scheme recently designed based on Shamir's scheme. This scheme has two main variants: one-time asynchronous and multiple-time asynchronous. In the one-time variant, it has been evaluated by the designer that the number of group members should be bounded to n <; kt + 1, in order to make the scheme resistant against outside attacks. This constraint has been relaxed in the multiple-time variant, backed by the hardness of the discrete logarithm problem. In this paper, we show that neither confining the number of group members nor using discrete logarithm have made the one-time and multiple-time variants of this scheme resistant against impersonation attack. We show that, in both cases, an outside attacker can impersonate an authorized group member in a polynomial time, when at least t + k-1 authorized members are participating in the group authentication session. The main observation, based on which the attack works, is that the dimension of the Linear Subspace spanned by the Lagrange components for any predefined set of users never exceeds t +k-1.

  • Linear Subspace Cryptanalysis of Harn’s Secret Sharing-Based Group Authentication Scheme
    IEEE Transactions on Information Forensics and Security, 2018
    Co-Authors: Zahra Ahmadian, Sadegh Jamshidpour
    Abstract:

    Shamir's secret sharing is used as an important underlying primitive in many other cryptographic schemes, such as group authentication and group key agreement schemes. Although Shamir secret sharing has unconditional security, it is not necessarily the case for the protocols founded on that. A common imperfect assumption in such schemes is to be satisfied of only hiding the polynomials coefficients from the adversary. In this direction, we present a new method that can be potentially used for cryptanalysis of some Shamir's secret sharing-based schemes. This method is called the Linear Subspace cryptanalysis, in which the attack problem is made equivalent to the problem of studying the belongingness of a vector to a given Linear Subspace. Using the proposed method, we analyse the Harn's group authentication protocol, which is a remarkable scheme recently designed based on Shamir's scheme. This scheme has two main variants: one-time asynchronous and multiple-time asynchronous. In the one-time variant, it has been evaluated by the designer that the number of group members should be bounded to n

Zahra Ahmadian - One of the best experts on this subject based on the ideXlab platform.

  • Linear Subspace cryptanalysis of harn s secret sharing based group authentication scheme
    IEEE Transactions on Information Forensics and Security, 2018
    Co-Authors: Zahra Ahmadian, Sadegh Jamshidpour
    Abstract:

    Shamir's secret sharing is used as an important underlying primitive in many other cryptographic schemes, such as group authentication and group key agreement schemes. Although Shamir secret sharing has unconditional security, it is not necessarily the case for the protocols founded on that. A common imperfect assumption in such schemes is to be satisfied of only hiding the polynomials coefficients from the adversary. In this direction, we present a new method that can be potentially used for cryptanalysis of some Shamir's secret sharing-based schemes. This method is called the Linear Subspace cryptanalysis, in which the attack problem is made equivalent to the problem of studying the belongingness of a vector to a given Linear Subspace. Using the proposed method, we analyse the Harn's group authentication protocol, which is a remarkable scheme recently designed based on Shamir's scheme. This scheme has two main variants: one-time asynchronous and multiple-time asynchronous. In the one-time variant, it has been evaluated by the designer that the number of group members should be bounded to n <; kt + 1, in order to make the scheme resistant against outside attacks. This constraint has been relaxed in the multiple-time variant, backed by the hardness of the discrete logarithm problem. In this paper, we show that neither confining the number of group members nor using discrete logarithm have made the one-time and multiple-time variants of this scheme resistant against impersonation attack. We show that, in both cases, an outside attacker can impersonate an authorized group member in a polynomial time, when at least t + k-1 authorized members are participating in the group authentication session. The main observation, based on which the attack works, is that the dimension of the Linear Subspace spanned by the Lagrange components for any predefined set of users never exceeds t +k-1.

  • Linear Subspace Cryptanalysis of Harn’s Secret Sharing-Based Group Authentication Scheme
    IEEE Transactions on Information Forensics and Security, 2018
    Co-Authors: Zahra Ahmadian, Sadegh Jamshidpour
    Abstract:

    Shamir's secret sharing is used as an important underlying primitive in many other cryptographic schemes, such as group authentication and group key agreement schemes. Although Shamir secret sharing has unconditional security, it is not necessarily the case for the protocols founded on that. A common imperfect assumption in such schemes is to be satisfied of only hiding the polynomials coefficients from the adversary. In this direction, we present a new method that can be potentially used for cryptanalysis of some Shamir's secret sharing-based schemes. This method is called the Linear Subspace cryptanalysis, in which the attack problem is made equivalent to the problem of studying the belongingness of a vector to a given Linear Subspace. Using the proposed method, we analyse the Harn's group authentication protocol, which is a remarkable scheme recently designed based on Shamir's scheme. This scheme has two main variants: one-time asynchronous and multiple-time asynchronous. In the one-time variant, it has been evaluated by the designer that the number of group members should be bounded to n

Yide Wang - One of the best experts on this subject based on the ideXlab platform.

  • ESPRITWED-UG and AV-ESPRITWED: Two new Linear Subspace algorithms for time delay estimation
    2008 16th European Signal Processing Conference, 2008
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang, Xavier Dérobert, Joseph Saillard
    Abstract:

    Two improvements of the “Linear ESPRIT” algorithm in [1] are proposed and applied to the time delay estimation (TDE) from radar data within the microwave range. At first, “Linear ESPRIT” is adapted to the TDE. Then, two new Linear Subspace algorithms, namely ESPRITWED-UG (ESPRIT Without EigenDecomposition) and AV-ESPRITWED (AVerage ESPRIT Without EigenDecomposition) are proposed. Contrary to [1], both algorithms take the whole bandwidth into account. Computer tests enable to assess the performances of the algorithms on the measurements of the layer thickness of civil engineering materials from GPR data. The new methods show improved noise robustness and smaller standard deviation in comparison with Linear ESPRIT [1] and SWEDE [2].

  • EUSIPCO - Some improvements of the Linear Subspace algorithm swede for time delay estimation
    2007
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang
    Abstract:

    In this paper, some improvements on the Linear Subspace algorithm SWEDE are proposed in order to adapt this algorithm to the estimation of the time delay from radar data in microwave range. At first, the radar pulse is taken into account in the algorithm. Then, the noise whitening technique in [2] is improved by a full block diagonalization of the noise covariance matrix. Finally, SWEDE is used in conjunction with ESPRIT to make the time delay estimation more efficient. Both algorithms are tested on simulated data to measure the layer thickness of materials for civil engineering applications.

  • Some improvements of the Linear Subspace algorithm swede for time delay estimation
    2007 15th European Signal Processing Conference, 2007
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang
    Abstract:

    In this paper, some improvements on the Linear Subspace algorithm SWEDE are proposed in order to adapt this algorithm to the estimation of the time delay from radar data in microwave range. At first, the radar pulse is taken into account in the algorithm. Then, the noise whitening technique in [2] is improved by a full block diagonalization of the noise covariance matrix. Finally, SWEDE is used in conjunction with ESPRIT to make the time delay estimation more efficient. Both algorithms are tested on simulated data to measure the layer thickness of materials for civil engineering applications.

Cédric Le Bastard - One of the best experts on this subject based on the ideXlab platform.

  • ESPRITWED-UG and AV-ESPRITWED: Two new Linear Subspace algorithms for time delay estimation
    2008 16th European Signal Processing Conference, 2008
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang, Xavier Dérobert, Joseph Saillard
    Abstract:

    Two improvements of the “Linear ESPRIT” algorithm in [1] are proposed and applied to the time delay estimation (TDE) from radar data within the microwave range. At first, “Linear ESPRIT” is adapted to the TDE. Then, two new Linear Subspace algorithms, namely ESPRITWED-UG (ESPRIT Without EigenDecomposition) and AV-ESPRITWED (AVerage ESPRIT Without EigenDecomposition) are proposed. Contrary to [1], both algorithms take the whole bandwidth into account. Computer tests enable to assess the performances of the algorithms on the measurements of the layer thickness of civil engineering materials from GPR data. The new methods show improved noise robustness and smaller standard deviation in comparison with Linear ESPRIT [1] and SWEDE [2].

  • EUSIPCO - Some improvements of the Linear Subspace algorithm swede for time delay estimation
    2007
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang
    Abstract:

    In this paper, some improvements on the Linear Subspace algorithm SWEDE are proposed in order to adapt this algorithm to the estimation of the time delay from radar data in microwave range. At first, the radar pulse is taken into account in the algorithm. Then, the noise whitening technique in [2] is improved by a full block diagonalization of the noise covariance matrix. Finally, SWEDE is used in conjunction with ESPRIT to make the time delay estimation more efficient. Both algorithms are tested on simulated data to measure the layer thickness of materials for civil engineering applications.

  • Some improvements of the Linear Subspace algorithm swede for time delay estimation
    2007 15th European Signal Processing Conference, 2007
    Co-Authors: Cédric Le Bastard, Vincent Baltazart, Yide Wang
    Abstract:

    In this paper, some improvements on the Linear Subspace algorithm SWEDE are proposed in order to adapt this algorithm to the estimation of the time delay from radar data in microwave range. At first, the radar pulse is taken into account in the algorithm. Then, the noise whitening technique in [2] is improved by a full block diagonalization of the noise covariance matrix. Finally, SWEDE is used in conjunction with ESPRIT to make the time delay estimation more efficient. Both algorithms are tested on simulated data to measure the layer thickness of materials for civil engineering applications.

T. Okatani - One of the best experts on this subject based on the ideXlab platform.

  • CVPR Workshops - A Probabilistic Approach to Linear Subspace Fitting for Computer Vision Problems
    2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004
    Co-Authors: T. Okatani
    Abstract:

    Several computer vision problems, such as some of photometric problems and the problem of affine structure from motion, are formulated as fitting Linear Subspace(s) to point data in a multi-dimensional space. In ideal cases the Linear Subspaces can easily be computed by PCA/SVD algorithms. Unfortunately this will not apply to real cases, since there are outliers and missing components in real data. Furthermore it is sometimes necessary to fit multiple different Subspaces to a set of point data in a situation where each point belongs to one of the Subspaces but it is unknown which Subspace each point belongs to. One straightforward solution to these advanced cases is to adopt the expectation maximization framework based on Bayesian inference. However, this solution does not seem to have been well considered in computer vision community, as far as the above problems of Linear Subspace fitting are concerned. This paper presents expectation maximization algorithms and its extension, variational Bayes-based algorithm, for several cases of Linear Subspace fitting and applies them to computer vision problems.

  • A Probabilistic Approach to Linear Subspace Fitting for Computer Vision Problems
    2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004
    Co-Authors: T. Okatani
    Abstract:

    Several computer vision problems, such as some of photometric problems and the problem of affine structure from motion, are formulated as fitting Linear Subspace(s) to point data in a multi-dimensional space. In ideal cases the Linear Subspaces can easily be computed by PCA/SVD algorithms. Unfortunately this will not apply to real cases, since there are outliers and missing components in real data. Furthermore it is sometimes necessary to fit multiple different Subspaces to a set of point data in a situation where each point belongs to one of the Subspaces but it is unknown which Subspace each point belongs to. One straightforward solution to these advanced cases is to adopt the expectation maximization framework based on Bayesian inference. However, this solution does not seem to have been well considered in computer vision community, as far as the above problems of Linear Subspace fitting are concerned. This paper presents expectation maximization algorithms and its extension, variational Bayes-based algorithm, for several cases of Linear Subspace fitting and applies them to computer vision problems.