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Modular Inverse for Integers using Fast Constant Time GCD Algorithm and its Applications

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Modular Inverse for Integers using Fast Constant Time GCD Al...

International Conference on Field Programmable Logic and Applications

作者： Sanjay Deshpande Santos Merino del Pozo Victor Mateu Marc Manzano Najwa Aaraj Jakub Szefer Computer Architecture and Security Laboratory Yale University New Haven CT USA Cryptography Research Centre Technology Innovation Institute Abu Dhabi UAE Digital Security Group Faculty of Science Radboud University Nijmegen Netherlands Faculty of Engineering Mondragon Unibertsitatea Mondragon Spain Okinawa Institute of Science and Technology Okinawa Japan

Modular inversion, the multiplicative inverse of an integer in the ring of integers modulo a prime number, is widely used in public-key cryptography. However, it is one of the most computationally intensive operations, thus, it remains the main performance bottleneck for many cryptographic *** paper presents to the best of the author’s knowledge, the first FPGA-based hardware design for computing the multiplicative inverse using the recently proposed fast constant-time Greatest Common Divisor (GCD) algorithm. This paper introduces two distinct design architectures targeting different applications: (a) a full-width design and (b) a sequential design. The presented designs are compact, parameterizable, and scalable in terms of area and speed. The evaluation shows the proposed designs, which are constant-time and protect against timing-based attacks, outperform existing software and hardware implementations that use other modular inversion techniques. As a specific example, this work presents an evaluation focusing on the use of the multiplicative inverse hardware module to accelerate the ElGamal cryptosystem. The proposed design achieves a speed-up of 90% in the modular inverse calculation and a speed-up of 45% in the overall ElGamal decryption algorithm using our sequential hardware design of fast constant-time GCD *** addition to developing the fast hardware implementation, this work potentially opens up a new direction for designing cryptosystems: the inverse operation is often avoided when designing algorithms, due to its complexity. With the new hardware module, using the inverse becomes more tractable, making it more appealing to use in the design of new cryptosystems.

关键词： Software algorithms Focusing computer architecture Public key cryptography Hardware Software Complexity theory

Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

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Enhanced Particle Swarm Optimization for Workflow Scheduling...

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

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An improved RL-based framework for multiple biomedical event...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhao, Yao Zhao, Weizhong Jiang, Xingpeng He, Tingting Su, Bianping Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Hubei Wuhan430079 China School of Computer Central China Normal University Hubei Wuhan430079 China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Hubei Wuhan430079 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China Xi'An University of Architecture and Technology Shanxi Xi'an China

ISBN: (纸本)9781665401265

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method. © 2021 IEEE.

关键词： Reinforcement learning

SPECBOX: A label-based transparent speculation scheme against transient execution attacks

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arXiv 2021年

作者： Tang, Bowen Wu, Chenggang Wang, Zhe Jia, Lichen Yew, Pen-Chung Cheng, Yueqiang Zhang, Yinqian Wang, Chenxi Xu, Guoqing Harry State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of the Chinese Academy of Sciences China University of Minnesota Twin Cities United States NIO Security Research United States Southern University of Science and Technology China University of California Los Angeles United States

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead;(2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SPECBOX. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SPECBOX also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SPECBOX is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital storage

OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

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arXiv 2021年

作者： Huang, Yunyou Wang, Nana Tang, Suqin Ma, Li Hao, Tianshu Jiang, Zihan Zhang, Fan Kang, Guoxin Miao, Xiuxia Guan, Xianglong Zhang, Ruchang Zhang, Zhifei Zhan, Jianfeng Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China University of Chinese Academy of Sciences China International Open Benchmark Council

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer’s disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services. © 2021, CC BY.

关键词： Diagnosis

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

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arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Xiao, Liang Wu, Qingqing School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Information and Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Optimization Space Exploration of Hardware Design for CRYSTALS-KYBER

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Optimization Space Exploration of Hardware Design for CRYSTA...

Asian Test Symposium (ATS)

作者： Yixuan Zhao Zhiteng Chao Jing Ye Wen Wang Yuan Cao Shuai Chen Xiaowei Li Huawei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Yale University US Hohai University China Rock Solid Security Lab FiberHome Co. Ltd. China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Public key cryptography is important in the global communication digital infrastructure. However, the emergence of quantum computer and Shor algorithm has greatly threatened the security of public key cryptography. The CRYSTALS-KYBER, as a lattice-based KEM algorithm, passed three rounds of a global solicitation for post-quantum cryptography algorithms held by the National Institute of Standards and Technology (NIST). This paper explores the implementation and optimization space of hardware design according to CRYSTALS-KYBER algorithm. We analyze its software code and try different strategies to optimize the hardware implementation, and conduct comparative analysis in terms of area and speed. The experimental results show that the performance can be greatly improved by moderately optimizing the loops. In comparison with optimal results of the work [12], our optimizations improve the performance by up to 74.6% for encapsulation algorithm and 54.4% for decapsulation algorithm.

关键词： Cryptography Optimization Hardware Pipeline processing Encapsulation computers Space exploration

Eliminating NB-IoT interference to LTE system: A sparse machine learning based approach

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arXiv 2020年

作者： Liu, Sicong Xiao, Liang Han, Zhu Tang, Yuliang Department of Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

—Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

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An improved RL-based framework for multiple biomedical event...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Zhao Weizhong Zhao Xingpeng Jiang Tingting He Bianping Su Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin PR China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin PR China Xi’an University of Architecture and Technology Xi’an Shanxi PR China

ISBN: (纸本)9781665429825

关键词： Training Annotations Conferences Biological system modeling Data models Data mining Bioinformatics