The Experts below are selected from a list of 2613 Experts worldwide ranked by ideXlab platform
Volta, Francesca Dalla - One of the best experts on this subject based on the ideXlab platform.
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On the primitivity of the AES key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
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On the primitivity of the AES-128 key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
Aragona Riccardo - One of the best experts on this subject based on the ideXlab platform.
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On the primitivity of the AES key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
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On the primitivity of the AES-128 key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
Civino Roberto - One of the best experts on this subject based on the ideXlab platform.
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On the primitivity of the AES key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
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On the primitivity of the AES-128 key-schedule
2021Co-Authors: Aragona Riccardo, Civino Roberto, Volta, Francesca DallaAbstract:The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group $\Gamma$ generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing $\Gamma$ and all the translations of the message space is primitive. As a consequence, we obtain that no proper and non-Trivial Subspace can be invariant under its action
Amitava Datta - One of the best experts on this subject based on the ideXlab platform.
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a novel algorithm for fast and scalable Subspace clustering of high dimensional data
Journal of Big Data, 2015Co-Authors: Amardeep Kaur, Amitava DattaAbstract:Rapid growth of high dimensional datasets in recent years has created an emergent need to extract the knowledge underlying them. Clustering is the process of automatically finding groups of similar data points in the space of the dimensions or attributes of a dataset. Finding clusters in the high dimensional datasets is an important and challenging data mining problem. Data group together differently under different subsets of dimensions, called Subspaces. Quite often a dataset can be better understood by clustering it in its Subspaces, a process called Subspace clustering. But the exponential growth in the number of these Subspaces with the dimensionality of data makes the whole process of Subspace clustering computationally very expensive. There is a growing demand for efficient and scalable Subspace clustering solutions in many Big data application domains like biology, computer vision, astronomy and social networking. Apriori based hierarchical clustering is a promising approach to find all possible higher dimensional Subspace clusters from the lower dimensional clusters using a bottom-up process. However, the performance of the existing algorithms based on this approach deteriorates drastically with the increase in the number of dimensions. Most of these algorithms require multiple database scans and generate a large number of redundant Subspace clusters, either implicitly or explicitly, during the clustering process. In this paper, we present SUBSCALE, a novel clustering algorithm to find non-Trivial Subspace clusters with minimal cost and it requires only k database scans for a k-dimensional data set. Our algorithm scales very well with the dimensionality of the dataset and is highly parallelizable. We present the details of the SUBSCALE algorithm and its evaluation in this paper.
Amardeep Kaur - One of the best experts on this subject based on the ideXlab platform.
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a novel algorithm for fast and scalable Subspace clustering of high dimensional data
Journal of Big Data, 2015Co-Authors: Amardeep Kaur, Amitava DattaAbstract:Rapid growth of high dimensional datasets in recent years has created an emergent need to extract the knowledge underlying them. Clustering is the process of automatically finding groups of similar data points in the space of the dimensions or attributes of a dataset. Finding clusters in the high dimensional datasets is an important and challenging data mining problem. Data group together differently under different subsets of dimensions, called Subspaces. Quite often a dataset can be better understood by clustering it in its Subspaces, a process called Subspace clustering. But the exponential growth in the number of these Subspaces with the dimensionality of data makes the whole process of Subspace clustering computationally very expensive. There is a growing demand for efficient and scalable Subspace clustering solutions in many Big data application domains like biology, computer vision, astronomy and social networking. Apriori based hierarchical clustering is a promising approach to find all possible higher dimensional Subspace clusters from the lower dimensional clusters using a bottom-up process. However, the performance of the existing algorithms based on this approach deteriorates drastically with the increase in the number of dimensions. Most of these algorithms require multiple database scans and generate a large number of redundant Subspace clusters, either implicitly or explicitly, during the clustering process. In this paper, we present SUBSCALE, a novel clustering algorithm to find non-Trivial Subspace clusters with minimal cost and it requires only k database scans for a k-dimensional data set. Our algorithm scales very well with the dimensionality of the dataset and is highly parallelizable. We present the details of the SUBSCALE algorithm and its evaluation in this paper.
