A methodology is presented to obtain the basis of qudits which are admissible to quantum Fourier transform(QFT)in the sense that the set of such kets are related by the QFT in the same way as the kets of the computati...A methodology is presented to obtain the basis of qudits which are admissible to quantum Fourier transform(QFT)in the sense that the set of such kets are related by the QFT in the same way as the kets of the computational basis.We first study this method for qubits to characterize the ensemble that works for the Hadamard transformation(QFT for two dimension).In this regard we identify certain incompleteness in the result of Maitra and Parashar(Int.J.Quantum Inform.4(2006)653).Next we characterize the ensemble of qutrits for which QFT is possible.Further,some theoretical results related to higher dimensions are also discussed.Considering the unitary matrix Un related to QFT,the issue boils down to the problem of characterizing matrices that commute with Un.展开更多
As the fault‐based attacks are becoming a more pertinent threat in today's era of edge computing/internet‐of‐things,there is a need to streamline the existing tools for better accuracy and ease of use,so that w...As the fault‐based attacks are becoming a more pertinent threat in today's era of edge computing/internet‐of‐things,there is a need to streamline the existing tools for better accuracy and ease of use,so that we can gauge the attacker's power and a proper countermeasure can be devised in the long run.In this regard,we propose a machine learning(ML)assisted tool that can be used in the context of a differential fault attack.In particular,finding the exact fault location by analysing the output difference(typically the XOR of the nonfaulty and the faulty ciphertexts)is somewhat nontrivial.During the literature survey,we notice that the Pearson's correlation coefficient dominantly is used for this purpose,and has almost become the defacto standard.While this method can yield good accuracy for certain cases,we argue that an MLbased method is more powerful in all the situations we experiment with.We sub-stantiate our claim by showing the relative performances(we choose the commonly used multilayer perceptron as our ML tool)with two variants of Grain‐128a(a stream cipher,and a stream cipher with authentication),the lightweight stream cipher LIZARD and the lightweight block cipher SIMON‐32(where the faults are injected at the fifth last rounds).Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing data.We believe that our work extends the state‐of‐the‐art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.展开更多
文摘A methodology is presented to obtain the basis of qudits which are admissible to quantum Fourier transform(QFT)in the sense that the set of such kets are related by the QFT in the same way as the kets of the computational basis.We first study this method for qubits to characterize the ensemble that works for the Hadamard transformation(QFT for two dimension).In this regard we identify certain incompleteness in the result of Maitra and Parashar(Int.J.Quantum Inform.4(2006)653).Next we characterize the ensemble of qutrits for which QFT is possible.Further,some theoretical results related to higher dimensions are also discussed.Considering the unitary matrix Un related to QFT,the issue boils down to the problem of characterizing matrices that commute with Un.
文摘As the fault‐based attacks are becoming a more pertinent threat in today's era of edge computing/internet‐of‐things,there is a need to streamline the existing tools for better accuracy and ease of use,so that we can gauge the attacker's power and a proper countermeasure can be devised in the long run.In this regard,we propose a machine learning(ML)assisted tool that can be used in the context of a differential fault attack.In particular,finding the exact fault location by analysing the output difference(typically the XOR of the nonfaulty and the faulty ciphertexts)is somewhat nontrivial.During the literature survey,we notice that the Pearson's correlation coefficient dominantly is used for this purpose,and has almost become the defacto standard.While this method can yield good accuracy for certain cases,we argue that an MLbased method is more powerful in all the situations we experiment with.We sub-stantiate our claim by showing the relative performances(we choose the commonly used multilayer perceptron as our ML tool)with two variants of Grain‐128a(a stream cipher,and a stream cipher with authentication),the lightweight stream cipher LIZARD and the lightweight block cipher SIMON‐32(where the faults are injected at the fifth last rounds).Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing data.We believe that our work extends the state‐of‐the‐art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.
