The Experts below are selected from a list of 198495 Experts worldwide ranked by ideXlab platform
Farid Mokrane - One of the best experts on this subject based on the ideXlab platform.
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Spectral Approach to process the (multivariate) high-order template attack against any masking scheme
Journal of Cryptographic Engineering, 2021Co-Authors: Maamar Ouladj, Sylvain Guilley, Philippe Guillot, Farid MokraneAbstract:Cryptographic software is particularly vulnerable to side-channel attacks when programmed in embedded devices. Indeed, the leakage is particularly intense compared to the noise level, making it mandatory for the developer to implement side-channel attack protections. Random masking is a customary option, but in this case, the countermeasure must be high order, meaning that each sensitive variable is splitted into multiple (at least two) shares. Attacks therefore become computationally challenging. In this paper, we show that high-order template attacks can be expressed under the form of a convolution. This formulation allows for a considerable speed-up in their computation thanks to fast Fourier transforms. To further speed-up the attack, we also provide an interesting multi-threading implementation of this Approach. This strategy naturally applies to template attacks where the leakage of each share is multivariate. We show that this strategy can be adapted to several masking schemes, inherently to the way the splitting is realized. This technique allows us to validate multiple very high-order attacks (order of some tens). In particular, it has revealed a non-trivial flaw (hard to detect otherwise) in a multivariate extension of the DSM masking (and subsequently to fix it, and validate its rationale).
Malik Magdonismail - One of the best experts on this subject based on the ideXlab platform.
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communities and balance in signed networks a Spectral Approach
Advances in Social Networks Analysis and Mining, 2012Co-Authors: Pranay Anchuri, Malik MagdonismailAbstract:Discussion based websites like Epinions.com and Slashdot.com allow users to identify both friends and foes. Such networks are called Signed Social Networks and mining communities of like-minded users from these networks has potential value. We extend existing community detection algorithms that work only on unsigned networks to be applicable to signed networks. In particular, we develop a Spectral Approach augmented with iterative optimization. We use our algorithms to study both communities and structural balance. Our results indicate that modularity based communities are distinct from structurally balanced communities.
Eduard Baumohl - One of the best experts on this subject based on the ideXlab platform.
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are cryptocurrencies connected to forex a quantile cross Spectral Approach
Finance Research Letters, 2019Co-Authors: Eduard BaumohlAbstract:Abstract This paper analyzes the connectedness between forex and cryptocurrencies using the quantile cross-Spectral Approach. The sample covers six forex and six cryptocurrencies over the period of September 2015–December 2017. Compared with the results obtained from standard correlations and DMCA, the quantile cross-Spectral Approach provides richer information on the dependence structure across different quantiles and frequencies. The results show that there are some significant negative dependencies between forex and cryptocurrencies from both the short- and long-term perspectives; thus, it is worth diversifying between these two asset groups. Moreover, the connection between cryptocurrencies is not as strong as is widely believed.
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are cryptocurrencies connected to forex a quantile cross Spectral Approach
Research Papers in Economics, 2018Co-Authors: Eduard BaumohlAbstract:This paper aims to elucidate the connectedness between major forex currencies and cryptocurrencies using the quantile cross-Spectral Approach recently proposed by Barunik and Kley (2015). The sample covers six forex currencies and six cryptocurrencies over the period of 1 September 2015 to 29 December 2017. Compared with the results obtained from standard correlations and detrended moving-average cross-correlation analysis (DMCA), the quantile cross-Spectral Approach provides richer information on the dependence structure across different quantiles and frequencies. The most interesting result is that the intra-group dependencies are positive in the lower extreme quantiles, while inter-group dependencies are negative. This result holds in both the short- and long-term perspectives. Thus, it is worth diversifying between these two currency groups.
Maamar Ouladj - One of the best experts on this subject based on the ideXlab platform.
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Spectral Approach to process the (multivariate) high-order template attack against any masking scheme
Journal of Cryptographic Engineering, 2021Co-Authors: Maamar Ouladj, Sylvain Guilley, Philippe Guillot, Farid MokraneAbstract:Cryptographic software is particularly vulnerable to side-channel attacks when programmed in embedded devices. Indeed, the leakage is particularly intense compared to the noise level, making it mandatory for the developer to implement side-channel attack protections. Random masking is a customary option, but in this case, the countermeasure must be high order, meaning that each sensitive variable is splitted into multiple (at least two) shares. Attacks therefore become computationally challenging. In this paper, we show that high-order template attacks can be expressed under the form of a convolution. This formulation allows for a considerable speed-up in their computation thanks to fast Fourier transforms. To further speed-up the attack, we also provide an interesting multi-threading implementation of this Approach. This strategy naturally applies to template attacks where the leakage of each share is multivariate. We show that this strategy can be adapted to several masking schemes, inherently to the way the splitting is realized. This technique allows us to validate multiple very high-order attacks (order of some tens). In particular, it has revealed a non-trivial flaw (hard to detect otherwise) in a multivariate extension of the DSM masking (and subsequently to fix it, and validate its rationale).
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Spectral Approach to process the high order template attack against any masking scheme
2021Co-Authors: Maamar Ouladj, Sylvain GuilleyAbstract:Cryptographic devices manage secret keys, which must be protected against extraction. One stealthy attack consists in the analysis of side-channel leakage. As a countermeasure, cryptographic computations can be randomly masked.
Sham M Kakade - One of the best experts on this subject based on the ideXlab platform.
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a tensor Spectral Approach to learning mixed membership community models
Conference on Learning Theory, 2013Co-Authors: Anima Anandkumar, Daniel Hsu, Sham M KakadeAbstract:Detecting hidden communities from observed interactions is a classical problem. Theoretical analysis of community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced in Airoldi et al. (2008). This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified Approach to learning communities in these models via a tensor Spectral decomposition Approach. Our estimator uses low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is based on simple linear algebraic operations such as singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters, and present a careful finite sample analysis of our learning method. Additionally, our results match the best known scaling requirements for the special case of the (homogeneous) stochastic block model.
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a tensor Spectral Approach to learning mixed membership community models
2013Co-Authors: Anima Anandkumar, Daniel Hsu, Sham M KakadeAbstract:Community detection is the task of detecting hidden communities from observed interactions. Guaranteed community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we remove this restriction, and provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced by Airoldi et al. This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified Approach to learning these models via a tensor Spectral decomposition method. Our estimator is based on low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is fast and is based on simple linear algebraic operations, e.g. singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters and present a careful finite sample analysis of our learning method. As an important special case, our results match the best known scaling requirements for the (homogeneous) stochastic block model.
