The Chinese hash algorithm SM3 is verified to be secure enough,but improper hardware implementation may lead to leakage.A masking scheme for SM3 algorithm is proposed to ensure the security of SM3 based Message Authen...The Chinese hash algorithm SM3 is verified to be secure enough,but improper hardware implementation may lead to leakage.A masking scheme for SM3 algorithm is proposed to ensure the security of SM3 based Message Authentication Code(MAC).Our scheme was implemented in hardware,which utilizes hardware oriented secure conversion techniques between boolean and arithmetic masking.Security evaluation based on SAKURA-G FPGA board has been done with 2000 power traces from 2000 random plaintexts with random plaintext masks and random key masks.It has been verified that the masked SM3 hardware implementation shows no intermediate value leakage as expected.Our masked SM3 hardware can resist first-order correlation power attack(CPA) and collision correlation attack.展开更多
Due to its provable security and remarkable device-independence,masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks.However,relatively high cost of masking se...Due to its provable security and remarkable device-independence,masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks.However,relatively high cost of masking severely limits its applicability.Considering the high tackling complexity of non-linear operations,most masked AES implementations focus on the security and cost reduction of masked S-boxes.In this paper,we focus on linear operations,which seems to be underestimated,on the contrary.Specifically,we discover some security flaws and redundant processes in popular first-order masked AES linear operations,and pinpoint the underlying root causes.Then we propose a provably secure and highly efficient masking scheme for AES linear operations.In order to show its practical implications,we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal,while keeping their original non-linear operations unchanged.We implement four newly combined masking schemes on an Intel Core i7-4790 CPU,and the results show they are roughly 20%faster than those original ones.Then we select one masked implementation named RSMv2 due to its popularity,and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices.The results show that no exploitable first-order side-channel leakages are detected.Moreover,compared with original masked AES implementations,our combined approach is nearly 25%faster on the AVR processor,and at least 70%more efficient on four FPGA devices.展开更多
It is well known that the convergence of multivariate subdivision schemes with finite masks can be characterized via joint spectral radius. For nonnegative masks, we will present in this paper some computable simply s...It is well known that the convergence of multivariate subdivision schemes with finite masks can be characterized via joint spectral radius. For nonnegative masks, we will present in this paper some computable simply sufficient conditions for the convergence, which will cover a substantially large class of schemes.展开更多
Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is e...Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system.To improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of GTS.First,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability events.Then,linear action space is developed to reduce dimensionality of action space for multiple controllable generators.Especially,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process.This can enhance the efficiency and learning process.Moreover,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning ability.Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.展开更多
This paper is concerned with multivariate refinement equations of the type where (?) is the unknown function defined on the s-dimensional Euclidean space Rs, a is a finitely supported nonnegative sequence on Zs, and M...This paper is concerned with multivariate refinement equations of the type where (?) is the unknown function defined on the s-dimensional Euclidean space Rs, a is a finitely supported nonnegative sequence on Zs, and M is an s×s dilation matrix with m := |detM|. We characterize the existence of L2-solution of refinement equation in terms of spectral radius of a certain finite matrix or transition operator associated with refinement mask a and dilation matrix M. For s = 1 and M = 2, the sufficient and necessary conditions are obtained to characterize the existence of continuous solution of this refinement equation.展开更多
基金supported by the National Major Program "Core of Electronic Devices,High-End General Chips,and Basis of Software Products" of the Ministry of Industry and Information Technology of China (Nos.2014ZX01032205,2014ZX01032401001-Z05)the National Natural Science Foundation of China(No.61402252) "12th Five-Year Plan" The National Development Foundation for Cryptological Research(No. MMJJ201401009)
文摘The Chinese hash algorithm SM3 is verified to be secure enough,but improper hardware implementation may lead to leakage.A masking scheme for SM3 algorithm is proposed to ensure the security of SM3 based Message Authentication Code(MAC).Our scheme was implemented in hardware,which utilizes hardware oriented secure conversion techniques between boolean and arithmetic masking.Security evaluation based on SAKURA-G FPGA board has been done with 2000 power traces from 2000 random plaintexts with random plaintext masks and random key masks.It has been verified that the masked SM3 hardware implementation shows no intermediate value leakage as expected.Our masked SM3 hardware can resist first-order correlation power attack(CPA) and collision correlation attack.
基金National Natural Science Foundation of China(No.61632020,No.U1936209 and No.62002353)Beijing Natural Science Foundation(No.4192067).
文摘Due to its provable security and remarkable device-independence,masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks.However,relatively high cost of masking severely limits its applicability.Considering the high tackling complexity of non-linear operations,most masked AES implementations focus on the security and cost reduction of masked S-boxes.In this paper,we focus on linear operations,which seems to be underestimated,on the contrary.Specifically,we discover some security flaws and redundant processes in popular first-order masked AES linear operations,and pinpoint the underlying root causes.Then we propose a provably secure and highly efficient masking scheme for AES linear operations.In order to show its practical implications,we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal,while keeping their original non-linear operations unchanged.We implement four newly combined masking schemes on an Intel Core i7-4790 CPU,and the results show they are roughly 20%faster than those original ones.Then we select one masked implementation named RSMv2 due to its popularity,and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices.The results show that no exploitable first-order side-channel leakages are detected.Moreover,compared with original masked AES implementations,our combined approach is nearly 25%faster on the AVR processor,and at least 70%more efficient on four FPGA devices.
基金Supported by Zhejiang Provincial Natural Science Foundation of China (Grant Nos. Y1100440, Y1110491)Science & Technology Program of Zhejiang Province (Grant No. 2009C34006)+1 种基金Foundation of Zhejiang Educational Committee (Grant No. Y201018286)Major Science & Technology Projects of Zhejiang Province (Grant No. 2011C11050)
文摘It is well known that the convergence of multivariate subdivision schemes with finite masks can be characterized via joint spectral radius. For nonnegative masks, we will present in this paper some computable simply sufficient conditions for the convergence, which will cover a substantially large class of schemes.
基金supported by National Natural Science Foundation of China(No.U22B20111,No.U1866602)。
文摘Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system.To improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of GTS.First,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability events.Then,linear action space is developed to reduce dimensionality of action space for multiple controllable generators.Especially,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process.This can enhance the efficiency and learning process.Moreover,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning ability.Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.
基金supported by National Natural Science Foundation of China(Grant Nos.10071071&10471123).
文摘This paper is concerned with multivariate refinement equations of the type where (?) is the unknown function defined on the s-dimensional Euclidean space Rs, a is a finitely supported nonnegative sequence on Zs, and M is an s×s dilation matrix with m := |detM|. We characterize the existence of L2-solution of refinement equation in terms of spectral radius of a certain finite matrix or transition operator associated with refinement mask a and dilation matrix M. For s = 1 and M = 2, the sufficient and necessary conditions are obtained to characterize the existence of continuous solution of this refinement equation.
