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Volkher B Scholz - One of the best experts on this subject based on the ideXlab platform.
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quantum proof randomness Extractors via operator space theory
IEEE Transactions on Information Theory, 2017Co-Authors: Mario Berta, Omar Fawzi, Volkher B ScholzAbstract:Quantum-proof randomness Extractors are an important building block for classical and quantum cryptography as well as device independent randomness amplification and expansion. Furthermore, they are also a useful tool in quantum Shannon theory. It is known that some Extractor constructions are quantum-proof whereas others are provably not [Gavinsky et al. , STOC’07]. We argue that the theory of operator spaces offers a natural framework for studying to what extent Extractors are secure against quantum adversaries: we first phrase the definition of Extractors as a bounded norm condition between normed spaces, and then show that the presence of quantum adversaries corresponds to a completely bounded norm condition between operator spaces. From this, we show that very high min-entropy Extractors as well as Extractors with small output are always (approximately) quantum-proof. We also study a generalization of Extractors called randomness condensers. We phrase the definition of condensers as a bounded norm condition and the definition of quantum-proof condensers as a completely bounded norm condition. Seeing condensers as bipartite graphs, we then find that the bounded norm condition corresponds to an instance of a well-studied combinatorial problem, called bipartite densest subgraph. Furthermore, using the characterization in terms of operator spaces, we can associate to any condenser a Bell inequality (two-player game), such that classical and quantum strategies are in one-to-one correspondence with classical and quantum attacks on the condenser. Hence, we get for every quantum-proof condenser (which includes in particular quantum-proof Extractors) a Bell inequality that cannot be violated by quantum mechanics.
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quantum proof multi source randomness Extractors in the markov model
Conference on Theory of Quantum Computation Communication and Cryptography, 2016Co-Authors: Rotem Arnonfriedman, Christopher Portmann, Volkher B ScholzAbstract:Randomness Extractors, widely used in classical and quantum cryptography and other fields of computer science, e.g., derandomization, are functions which generate almost uniform randomness from weak sources of randomness. In the quantum setting one must take into account the quantum side information held by an adversary which might be used to break the security of the Extractor. In the case of seeded Extractors the presence of quantum side information has been extensively studied. For multi-source Extractors one can easily see that high conditional min-entropy is not sufficient to guarantee security against arbitrary side information, even in the classical case. Hence, the interesting question is under which models of (both quantum and classical) side information multi-source Extractors remain secure. In this work we suggest a natural model of side information, which we call the Markov model, and prove that any multi-source Extractor remains secure in the presence of quantum side information of this type (albeit with weaker parameters). This improves on previous results in which more restricted models were considered or the security of only some types of Extractors was shown.
Mario Berta - One of the best experts on this subject based on the ideXlab platform.
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quantum proof randomness Extractors via operator space theory
IEEE Transactions on Information Theory, 2017Co-Authors: Mario Berta, Omar Fawzi, Volkher B ScholzAbstract:Quantum-proof randomness Extractors are an important building block for classical and quantum cryptography as well as device independent randomness amplification and expansion. Furthermore, they are also a useful tool in quantum Shannon theory. It is known that some Extractor constructions are quantum-proof whereas others are provably not [Gavinsky et al. , STOC’07]. We argue that the theory of operator spaces offers a natural framework for studying to what extent Extractors are secure against quantum adversaries: we first phrase the definition of Extractors as a bounded norm condition between normed spaces, and then show that the presence of quantum adversaries corresponds to a completely bounded norm condition between operator spaces. From this, we show that very high min-entropy Extractors as well as Extractors with small output are always (approximately) quantum-proof. We also study a generalization of Extractors called randomness condensers. We phrase the definition of condensers as a bounded norm condition and the definition of quantum-proof condensers as a completely bounded norm condition. Seeing condensers as bipartite graphs, we then find that the bounded norm condition corresponds to an instance of a well-studied combinatorial problem, called bipartite densest subgraph. Furthermore, using the characterization in terms of operator spaces, we can associate to any condenser a Bell inequality (two-player game), such that classical and quantum strategies are in one-to-one correspondence with classical and quantum attacks on the condenser. Hence, we get for every quantum-proof condenser (which includes in particular quantum-proof Extractors) a Bell inequality that cannot be violated by quantum mechanics.
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Quantum to Classical Randomness Extractors
Advances in Cryptology – CRYPTO 2012, 2012Co-Authors: Mario Berta, Omar Fawzi, Stephanie WehnerAbstract:The goal of randomness extraction is to distill (almost) perfect randomness from a weak source of randomness. When the source outputs a classical string X , many Extractor constructions are known. Yet, when considering a physical randomness source, X is itself ultimately the result of a measurement on an underlying quantum system. When characterizing the power of a source to supply randomness it is hence a natural question to ask, how much classical randomness we can extract from a quantum system. To tackle this question we here take on the study of quantum-to-classical randomness Extractors (QC-Extractors). We provide constructions of QC-Extractors based on measurements in a full set of mutually unbiased bases (MUBs), and certain single qubit measurements. The latter are particularly appealing since they are not only easy to implement, but appear throughout quantum cryptography. We proceed to prove an upper bound on the maximum amount of randomness that we could hope to extract from any quantum state. Some of our QC-Extractors almost match this bound. We show two applications of our results. First, we show that any QC-Extractor gives rise to entropic uncertainty relations with respect to quantum side information. Such relations were previously only known for two measurements. In particular, we obtain strong relations in terms of the von Neumann (Shannon) entropy as well as the min-entropy for measurements in (almost) unitary 2-designs, a full set of MUBs, and single qubit measurements in three MUBs each. Second, we finally resolve the central open question in the noisy-storage model [Wehner et al., PRL 100, 220502 (2008)] by linking security to the quantum capacity of the adversary’s storage device. More precisely, we show that any two-party cryptographic primitive can be implemented securely as long as the adversary’s storage device has sufficiently low quantum capacity. Our protocol does not need any quantum storage to implement, and is technologically feasible using present-day technology.
Jianjun Wu - One of the best experts on this subject based on the ideXlab platform.
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Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning
IEEE Access, 2020Co-Authors: Feng Yi, Bo Jiang, Lu Wang, Jianjun WuAbstract:Cybersecurity named entity recognition is an important part of threat information extraction from large-scale unstructured text collection in many cybersecurity applications. Most existing security entity recognition studies and systems use regular matching strategy or machine learning algorithms. Due to the peculiarity and complexity of security named entity, these models ignore the characteristic of security data and the correlation of entities. Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional random fields (CRF) combined with four feature templates. This model is named RDF-CRF. The rule-based expressions can match security entities with good accuracy in simpler situations, the known-entity dictionary can extract common and specific security entity, and the CRF-based Extractor leverages the identified entities by rule-based and dictionary-based Extractors to further improve the recognition performance. In order to demonstrate the effectiveness of our proposed model, extensive experiments are performed on a security text dataset collected from public security webs. The experimental results show that can achieve better performance than state-of-the-art methods.
Hong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Performance evaluation of visual SLAM using several feature Extractors
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Jonathan Klippenstein, Hong ZhangAbstract:Visual simultaneous localization and mapping (SLAM) implementations must use feature extraction to reduce the dimensionality of image input, yet no comparison of feature Extractors exists in the context of visual SLAM. This paper presents both a method for comparison of visual SLAM performance using several different feature Extractors and the first experimental study using this method. Possible evaluation metrics are discussed and consistency testing and accumulated uncertainty are chosen to measure performance. Three feature Extractors commonly used for visual SLAM are examined: the Harris corner detector, the Kanade-Lucas-Tomasi tracker, and the scale-invariant feature transform. All three are found to perform similarly in an indoor test environment, close to or within the limits of measurement. A modest scale change is handled without difficulty. We conclude that feature Extractor choice is not significant in terms of visual SLAM performance and other criteria may be used to make the selection.
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Quantitative evaluation of feature Extractors for visual SLAM
Proceedings - Fourth Canadian Conference on Computer and Robot Vision CRV 2007, 2007Co-Authors: Jonathan Klippenstein, Hong ZhangAbstract:We present a performance evaluation framework for visual feature extraction and matching in the visual simultaneous localization and mapping (SLAM) context. Although feature extraction is a crucial component, no qualitative study comparing different techniques from the visual SLAM perspective exists. We extend previous image pair evaluation methods to handle non-planar scenes and the multiple image sequence requirements of our application, and compare three popular feature Extractors used in visual SLAM: the Harris corner detector, the Kanade-Lucas-Tomasi tracker (KLT), and the scale-invariant feature transform (SIFT). We present results from a typical indoor environment in the form of recall/precision curves, and also investigate the effect of increasing distance between image viewpoints on Extractor performance. Our results show that all methods can be made to perform well, although it is possible to distinguish between the three. We conclude by presenting guidelines for selecting a feature Extractor for visual SLAM based on our experiments.
Christoph Studer - One of the best experts on this subject based on the ideXlab platform.
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Headless Horseman: Adversarial Attacks on Transfer Learning Models
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Avi Schwarzschild, Tom Goldstein, Christoph StuderAbstract:Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature Extractors. We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these headless attacks. We first demonstrate successful transfer attacks against a victim network using only its feature Extractor. This motivates the introduction of a label-blind adversarial attack. This transfer attack method does not require any information about the class-label space of the victim. Our attack lowers the accuracy of a ResNet18 trained on CIFAR10 by over 40%.
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Headless Horseman: Adversarial Attacks on Transfer Learning Models
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Avi Schwarzschild, Tom Goldstein, Christoph StuderAbstract:Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature Extractors. We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these \emph{headless attacks}. We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature Extractor. This motivates the introduction of a label-blind adversarial attack. This transfer attack method does not require any information about the class-label space of the victim. Our attack lowers the accuracy of a ResNet18 trained on CIFAR10 by over 40\%.
