Cryptography Acceleration in a RISC-V GPGPU
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A Quantitative Study of Advanced Encryption Standard Performance
United States Military Academy USMA Digital Commons West Point ETD 12-2018 A Quantitative Study of Advanced Encryption Standard Performance as it Relates to Cryptographic Attack Feasibility Daniel Hawthorne United States Military Academy, [email protected] Follow this and additional works at: https://digitalcommons.usmalibrary.org/faculty_etd Part of the Information Security Commons Recommended Citation Hawthorne, Daniel, "A Quantitative Study of Advanced Encryption Standard Performance as it Relates to Cryptographic Attack Feasibility" (2018). West Point ETD. 9. https://digitalcommons.usmalibrary.org/faculty_etd/9 This Doctoral Dissertation is brought to you for free and open access by USMA Digital Commons. It has been accepted for inclusion in West Point ETD by an authorized administrator of USMA Digital Commons. For more information, please contact [email protected]. A QUANTITATIVE STUDY OF ADVANCED ENCRYPTION STANDARD PERFORMANCE AS IT RELATES TO CRYPTOGRAPHIC ATTACK FEASIBILITY A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Computer Science By Daniel Stephen Hawthorne Colorado Technical University December, 2018 Committee Dr. Richard Livingood, Ph.D., Chair Dr. Kelly Hughes, DCS, Committee Member Dr. James O. Webb, Ph.D., Committee Member December 17, 2018 © Daniel Stephen Hawthorne, 2018 1 Abstract The advanced encryption standard (AES) is the premier symmetric key cryptosystem in use today. Given its prevalence, the security provided by AES is of utmost importance. Technology is advancing at an incredible rate, in both capability and popularity, much faster than its rate of advancement in the late 1990s when AES was selected as the replacement standard for DES. Although the literature surrounding AES is robust, most studies fall into either theoretical or practical yet infeasible. -
Hoja De Datos De Familes Del Procesador Intel(R) Core(TM) De 10A Generación, Vol.1
10a generación de familias de procesadores Intel® Core™ Ficha técnica, Volumen 1 de 2 Compatible con la 10a generación de la familia de procesadores Intel® Core™, procesadores Intel® Pentium®, procesadores Intel® Celeron® para plataformas U/Y, anteriormente conocidos como Ice Lake. Agosto de 2019 Revisión 001 Número del Documento: 341077-001 Líneas legales y descargos de responsabilidad Esta información es una combinación de una traducción hecha por humanos y de la traducción automática por computadora del contenido original para su conveniencia. Este contenido se ofrece únicamente como información general y no debe ser considerada como completa o precisa. No puede utilizar ni facilitar el uso de este documento en relación con ninguna infracción u otro análisis legal relacionado con los productos Intel descritos en este documento. Usted acepta conceder a Intel una licencia no exclusiva y libre de regalías a cualquier reclamación de patente redactada posteriormente que incluya el objeto divulgado en este documento. Este documento no concede ninguna licencia (expresa o implícita, por impedimento o de otro tipo) a ningún derecho de propiedad intelectual. Las características y beneficios de las tecnologías Intel dependen de la configuración del sistema y pueden requerir la activación de hardware, software o servicio habilitado. El desempeño varía según la configuración del sistema. Ningún equipo puede ser absolutamente seguro. Consulte al fabricante de su sistema o su distribuidor minorista u obtenga más información en intel.la. Las tecnologías Intel pueden requerir la activación de hardware habilitado, software específico o servicios. Consulte con el fabricante o distribuidor del sistema. Los productos descritos pueden contener defectos de diseño o errores conocidos como erratas que pueden hacer que el producto se desvíe de las especificaciones publicadas. -
GPU Developments 2018
GPU Developments 2018 2018 GPU Developments 2018 © Copyright Jon Peddie Research 2019. All rights reserved. Reproduction in whole or in part is prohibited without written permission from Jon Peddie Research. This report is the property of Jon Peddie Research (JPR) and made available to a restricted number of clients only upon these terms and conditions. Agreement not to copy or disclose. This report and all future reports or other materials provided by JPR pursuant to this subscription (collectively, “Reports”) are protected by: (i) federal copyright, pursuant to the Copyright Act of 1976; and (ii) the nondisclosure provisions set forth immediately following. License, exclusive use, and agreement not to disclose. Reports are the trade secret property exclusively of JPR and are made available to a restricted number of clients, for their exclusive use and only upon the following terms and conditions. JPR grants site-wide license to read and utilize the information in the Reports, exclusively to the initial subscriber to the Reports, its subsidiaries, divisions, and employees (collectively, “Subscriber”). The Reports shall, at all times, be treated by Subscriber as proprietary and confidential documents, for internal use only. Subscriber agrees that it will not reproduce for or share any of the material in the Reports (“Material”) with any entity or individual other than Subscriber (“Shared Third Party”) (collectively, “Share” or “Sharing”), without the advance written permission of JPR. Subscriber shall be liable for any breach of this agreement and shall be subject to cancellation of its subscription to Reports. Without limiting this liability, Subscriber shall be liable for any damages suffered by JPR as a result of any Sharing of any Material, without advance written permission of JPR. -
SIMD Extensions
SIMD Extensions PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 12 May 2012 17:14:46 UTC Contents Articles SIMD 1 MMX (instruction set) 6 3DNow! 8 Streaming SIMD Extensions 12 SSE2 16 SSE3 18 SSSE3 20 SSE4 22 SSE5 26 Advanced Vector Extensions 28 CVT16 instruction set 31 XOP instruction set 31 References Article Sources and Contributors 33 Image Sources, Licenses and Contributors 34 Article Licenses License 35 SIMD 1 SIMD Single instruction Multiple instruction Single data SISD MISD Multiple data SIMD MIMD Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously. Thus, such machines exploit data level parallelism. History The first use of SIMD instructions was in vector supercomputers of the early 1970s such as the CDC Star-100 and the Texas Instruments ASC, which could operate on a vector of data with a single instruction. Vector processing was especially popularized by Cray in the 1970s and 1980s. Vector-processing architectures are now considered separate from SIMD machines, based on the fact that vector machines processed the vectors one word at a time through pipelined processors (though still based on a single instruction), whereas modern SIMD machines process all elements of the vector simultaneously.[1] The first era of modern SIMD machines was characterized by massively parallel processing-style supercomputers such as the Thinking Machines CM-1 and CM-2. These machines had many limited-functionality processors that would work in parallel. -
On Security and Privacy for Networked Information Society
Antti Hakkala On Security and Privacy for Networked Information Society Observations and Solutions for Security Engineering and Trust Building in Advanced Societal Processes Turku Centre for Computer Science TUCS Dissertations No 225, November 2017 ON SECURITY AND PRIVACY FOR NETWORKED INFORMATIONSOCIETY Observations and Solutions for Security Engineering and Trust Building in Advanced Societal Processes antti hakkala To be presented, with the permission of the Faculty of Mathematics and Natural Sciences of the University of Turku, for public criticism in Auditorium XXII on November 18th, 2017, at 12 noon. University of Turku Department of Future Technologies FI-20014 Turun yliopisto 2017 supervisors Adjunct professor Seppo Virtanen, D. Sc. (Tech.) Department of Future Technologies University of Turku Turku, Finland Professor Jouni Isoaho, D. Sc. (Tech.) Department of Future Technologies University of Turku Turku, Finland reviewers Professor Tuomas Aura Department of Computer Science Aalto University Espoo, Finland Professor Olaf Maennel Department of Computer Science Tallinn University of Technology Tallinn, Estonia opponent Professor Jarno Limnéll Department of Communications and Networking Aalto University Espoo, Finland The originality of this thesis has been checked in accordance with the University of Turku quality assurance system using the Turnitin OriginalityCheck service ISBN 978-952-12-3607-5 (Online) ISSN 1239-1883 To my wife Maria, I am forever grateful for everything. Thank you. ABSTRACT Our society has developed into a networked information soci- ety, in which all aspects of human life are interconnected via the Internet — the backbone through which a significant part of communications traffic is routed. This makes the Internet ar- guably the most important piece of critical infrastructure in the world. -
Efficient Hashing Using the AES Instruction
Efficient Hashing Using the AES Instruction Set Joppe W. Bos1, Onur Özen1, and Martijn Stam2 1 Laboratory for Cryptologic Algorithms, EPFL, Station 14, CH-1015 Lausanne, Switzerland {joppe.bos,onur.ozen}@epfl.ch 2 Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, United Kingdom [email protected] Abstract. In this work, we provide a software benchmark for a large range of 256-bit blockcipher-based hash functions. We instantiate the underlying blockci- pher with AES, which allows us to exploit the recent AES instruction set (AES- NI). Since AES itself only outputs 128 bits, we consider double-block-length constructions, as well as (single-block-length) constructions based on RIJNDAEL- 256. Although we primarily target architectures supporting AES-NI, our frame- work has much broader applications by estimating the performance of these hash functions on any (micro-)architecture given AES-benchmark results. As far as we are aware, this is the first comprehensive performance comparison of multi- block-length hash functions in software. 1 Introduction Historically, the most popular way of constructing a hash function is to iterate a com- pression function that itself is based on a blockcipher (this idea dates back to Ra- bin [49]). This approach has the practical advantage—especially on resource-constrained devices—that only a single primitive is needed to implement two functionalities (namely encrypting and hashing). Moreover, trust in the blockcipher can be conferred to the cor- responding hash function. The wisdom of blockcipher-based hashing is still valid today. Indeed, the current cryptographic hash function standard SHA-2 and some of the SHA- 3 candidates are, or can be regarded as, blockcipher-based designs. -
Demystifying Internet of Things Security Successful Iot Device/Edge and Platform Security Deployment — Sunil Cheruvu Anil Kumar Ned Smith David M
Demystifying Internet of Things Security Successful IoT Device/Edge and Platform Security Deployment — Sunil Cheruvu Anil Kumar Ned Smith David M. Wheeler Demystifying Internet of Things Security Successful IoT Device/Edge and Platform Security Deployment Sunil Cheruvu Anil Kumar Ned Smith David M. Wheeler Demystifying Internet of Things Security: Successful IoT Device/Edge and Platform Security Deployment Sunil Cheruvu Anil Kumar Chandler, AZ, USA Chandler, AZ, USA Ned Smith David M. Wheeler Beaverton, OR, USA Gilbert, AZ, USA ISBN-13 (pbk): 978-1-4842-2895-1 ISBN-13 (electronic): 978-1-4842-2896-8 https://doi.org/10.1007/978-1-4842-2896-8 Copyright © 2020 by The Editor(s) (if applicable) and The Author(s) This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. -
FAST HASHING in CUDA Neville Walo Department of Computer
FAST HASHING IN CUDA Neville Walo Department of Computer Science ETH Zurich¨ Zurich,¨ Switzerland ABSTRACT cies, it is possible to use the compression function of SHA- Hash functions, such as SHA-256 [1], are extensively used 256 along with the Sarkar-Schellenberg composition prin- in cryptographic applications. However, SHA-256 cannot ciple [2] to create a parallel collision resistant hash function be parallelized due to sequential dependencies. Using the called PARSHA-256 [3]. Sarkar-Schellenberg composition principle [2] in combina- In this work, we try to accelerate hashing in CUDA [6]. tion with SHA-256 gives rise to PARSHA-256 [3], a parallel We have divided this project into two sub-projects. The first collision resistant hash function. We present efficient imple- one is the Bitcoin scenario, with the goal to calculate many mentations for both SHA-256 and PARSHA-256 in CUDA. independent SHA-256 computation in parallel. The second Our results demonstrate that for large messages PARSHA- case is PARSHA-256, where the goal is to implement the 256 can significantly outperform SHA-256. proposed algorithm efficiently in CUDA. Related work. To our knowledge there is no compara- ble implementation of PARSHA-256 which runs on a GPU. 1. INTRODUCTION There exists only the implementation of the original paper, Hash functions are one of the most important operations which uses multithreading [3]. On the other hand, there in cryptographic applications, like digital signature algo- are countless implementations of Bitcoin Miners in CUDA rithms, keyed-hash message authentication codes, encryp- [7, 8], as this was the most prominent way to mine Bitcoins tions and the generation of random numbers. -
Stream Cipher Designs: a Review
SCIENCE CHINA Information Sciences March 2020, Vol. 63 131101:1–131101:25 . REVIEW . https://doi.org/10.1007/s11432-018-9929-x Stream cipher designs: a review Lin JIAO1*, Yonglin HAO1 & Dengguo FENG1,2* 1 State Key Laboratory of Cryptology, Beijing 100878, China; 2 State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China Received 13 August 2018/Accepted 30 June 2019/Published online 10 February 2020 Abstract Stream cipher is an important branch of symmetric cryptosystems, which takes obvious advan- tages in speed and scale of hardware implementation. It is suitable for using in the cases of massive data transfer or resource constraints, and has always been a hot and central research topic in cryptography. With the rapid development of network and communication technology, cipher algorithms play more and more crucial role in information security. Simultaneously, the application environment of cipher algorithms is in- creasingly complex, which challenges the existing cipher algorithms and calls for novel suitable designs. To accommodate new strict requirements and provide systematic scientific basis for future designs, this paper reviews the development history of stream ciphers, classifies and summarizes the design principles of typical stream ciphers in groups, briefly discusses the advantages and weakness of various stream ciphers in terms of security and implementation. Finally, it tries to foresee the prospective design directions of stream ciphers. Keywords stream cipher, survey, lightweight, authenticated encryption, homomorphic encryption Citation Jiao L, Hao Y L, Feng D G. Stream cipher designs: a review. Sci China Inf Sci, 2020, 63(3): 131101, https://doi.org/10.1007/s11432-018-9929-x 1 Introduction The widely applied e-commerce, e-government, along with the fast developing cloud computing, big data, have triggered high demands in both efficiency and security of information processing. -
A Fast, Verified, Cross-Platform Cryptographic Provider
EverCrypt: A Fast, Verified, Cross-Platform Cryptographic Provider Jonathan Protzenko∗, Bryan Parnoz, Aymeric Fromherzz, Chris Hawblitzel∗, Marina Polubelovay, Karthikeyan Bhargavany Benjamin Beurdouchey, Joonwon Choi∗x, Antoine Delignat-Lavaud∗,Cedric´ Fournet∗, Natalia Kulatovay, Tahina Ramananandro∗, Aseem Rastogi∗, Nikhil Swamy∗, Christoph M. Wintersteiger∗, Santiago Zanella-Beguelin∗ ∗Microsoft Research zCarnegie Mellon University yInria xMIT Abstract—We present EverCrypt: a comprehensive collection prone (due in part to Intel and AMD reporting CPU features of verified, high-performance cryptographic functionalities avail- inconsistently [78]), with various cryptographic providers able via a carefully designed API. The API provably supports invoking illegal instructions on specific platforms [74], leading agility (choosing between multiple algorithms for the same functionality) and multiplexing (choosing between multiple im- to killed processes and even crashing kernels. plementations of the same algorithm). Through abstraction and Since a cryptographic provider is the linchpin of most zero-cost generic programming, we show how agility can simplify security-sensitive applications, its correctness and security are verification without sacrificing performance, and we demonstrate crucial. However, for most applications (e.g., TLS, cryptocur- how C and assembly can be composed and verified against rencies, or disk encryption), the provider is also on the critical shared specifications. We substantiate the effectiveness of these techniques with -
A Bibliography of Publications on Hashing Algorithms
A Bibliography of Publications on Hashing Algorithms Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 24 August 2021 Version 2.344 Title word cross-reference [BRM+09, BS91b, BS91a, CM01, Gir87, Ven86, WS93, War14, Coh97, Coh98, LHC05, QG89, QG90]. O(1) [FKS84]. O(log log n) #2 [Cer85]. [MN90]. O(log W ) [LS07b]. O(N) [HG77, MN90]. pn [Ack74]. π [FFGL10]. q 1 [PPS21]. 10 [GLM+10]. 11 [SY11]. 2 [OWZ14]. SL2 [MT16]. Z=p [Mue04]. + [EAA 16, GG92, HD72]. 2n [QG89, QG90]. + 3 [CBA94, Fly92, GG92, GK94, LMJC07, -approximate [SWQ 14]. -ary LDY+16, SYW+20, WSSO12]. 5=8 [Sch11]. [CC91, CLC92, Gui78, RRS07]. -Bit $62m [Nic17]. 64 [LK16]. ∗ [LNS93]. + [QG89, QG90, LK16, LK11]. -Body MT [WS93, War14]. -codes [Bie95]. -dimension [Omi88, Omi89a]. [HRB13]. 2 [AK98, QJ97]. A [Lyo83]. A∗ [MD97]. A2 [LHC05]. -dimensional [Yuv75]. + + -Functions [OOB12]. -gram [Bie95]. α [ABC 16]. b [LK11]. B [TB91]. + c [SWQ+14]. d [FPS17, PRM16]. f [LG78]. [Coh98, Ven86]. -Grams [Coh97, BRM 09]. 2 -Hash [BS91b, BS91a]. -Independence GL2(Fpn )[TNS20].H [DRS12]. H2A [CBB05]. K [Yuv75, APV07, CL85, CC91, [PT16, PT10a]. -mer [HC14, PNPC20]. CLC92, DKRT15, Die96, EFMRK+20, -min-wise [FPS17]. -Nearest [CL85]. + -partitions [DKRT15]. -Pipeline [PRM16]. FPS17, Gui78, HC14, LLG 17, PT10a, + PT16, PNPC20, RRS07, SS90b]. L [OOB12]. -probe [SS90b]. -Round [GLM 10, SY11]. -
Extracting and Mapping Industry 4.0 Technologies Using Wikipedia
Computers in Industry 100 (2018) 244–257 Contents lists available at ScienceDirect Computers in Industry journal homepage: www.elsevier.com/locate/compind Extracting and mapping industry 4.0 technologies using wikipedia T ⁎ Filippo Chiarelloa, , Leonello Trivellib, Andrea Bonaccorsia, Gualtiero Fantonic a Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, 2, 56126 Pisa, Italy b Department of Economics and Management, University of Pisa, Via Cosimo Ridolfi, 10, 56124 Pisa, Italy c Department of Mechanical, Nuclear and Production Engineering, University of Pisa, Largo Lucio Lazzarino, 2, 56126 Pisa, Italy ARTICLE INFO ABSTRACT Keywords: The explosion of the interest in the industry 4.0 generated a hype on both academia and business: the former is Industry 4.0 attracted for the opportunities given by the emergence of such a new field, the latter is pulled by incentives and Digital industry national investment plans. The Industry 4.0 technological field is not new but it is highly heterogeneous (actually Industrial IoT it is the aggregation point of more than 30 different fields of the technology). For this reason, many stakeholders Big data feel uncomfortable since they do not master the whole set of technologies, they manifested a lack of knowledge Digital currency and problems of communication with other domains. Programming languages Computing Actually such problem is twofold, on one side a common vocabulary that helps domain experts to have a Embedded systems mutual understanding is missing Riel et al. [1], on the other side, an overall standardization effort would be IoT beneficial to integrate existing terminologies in a reference architecture for the Industry 4.0 paradigm Smit et al.