research-article

Scalable and efficient implementation of correlation power analysis using graphics processing units (GPUs)

Authors:

David KaeliAuthors Info & Claims

HASP '14: Proceedings of the Third Workshop on Hardware and Architectural Support for Security and Privacy

Article No.: 10, Pages 1 - 8

https://doi.org/10.1145/2611765.2611775

Published: 15 June 2014 Publication History

Abstract

Correlation Power Analysis (CPA) is a commonly used side-channel attack (SCA) on cryptographic devices, which analyzes power consumption to extract secret information like cryptographic keys. In this work, we have developed an open-source side-channel evaluation platform to evaluate the resilience of a range of devices to SCAs. Our platform includes an experimental setup for power trace collection and a trace analysis library. The time and effort to extract key values can greatly hamper our ability to analyze a single device. In this paper, we describe our work to leverage a Graphics Processing Unit (GPU) to accelerate key extraction. We develop a parallel framework in the Open Computing Language (OpenCL). OpenCL allows our framework to remain portable across a range of processing devices including CPUs, GPUs, and FPGAs. We describe the capabilities of our side-channel evaluation platform, and demonstrate how we leverage parallel processing to provide for more efficient and scalable side-channel analysis.

References

[1]

T. Bartkewitz and K. Lemke-Rust. A high-performance implementation of differential power analysis on graphics cards. In Smart Card Research & Advanced Applications, pages 252--265. 2011.

Digital Library

[2]

E. Brier, C. Clavier, and F. Olivier. Correlation power analysis with a leakage model. In Cryptographic Hardware & Embedded Systems-CHES 2004, pages 16--29. 2004.

[3]

J. Daemen and V. Rijmen. The design of Rijndael: AES-the advanced encryption standard. Springer, 2002.

Digital Library

[4]

R. R. C. for Information Security). SASEBO (side-channel attack standard evaluation board). http://www.rcis.aist.go.jp/special/SASEBO.

[5]

R. R. C. for Information Security). SASEBO waveform acquisition programming guide. http://www.rcis.aist.go.jp/project/sasebo/download_prev/DPAcontestV3/Sasebo_Acquisition_ProgrammingGuide_Ver0.3_English.

[6]

B. Gaster, L. Howes, D. R. Kaeli, P. Mistry, and D. Schaa. Heterogeneous Computing with OpenCL: Revised OpenCL 1. Newnes, 2012.

Digital Library

[7]

K. Group. OpenCL supported devices. http://www.khronos.org/conformance/adopters/conformant-products#opencl.

[8]

W. Hnath. Differential power analysis side-channel attacks in cryptography. PhD thesis, Worcester Polytechnic Institute, 2010.

[9]

C. C. R. Incorporated). DPA workstation. http://www.cryptography.com/technology/dpa/workstation.html/.

[10]

P. Kocher, J. Jaffe, B. Jun, and P. Rohatgi. Introduction to differential power analysis. Journal of Cryptographic Engineering, 1(1):5--27, 2011.

[11]

S. J. Lee, S. C. Seo, H. Dong-Guk, H. Seokhie, and L. Sangjin. Acceleration of differential power analysis through the parallel use of GPU and CPU. IEICE transactions on fundamentals of electronics, communications and computer sciences, 93(9):1688--1692, 2010.

[12]

Nvidia. CUDA samples. http://docs.nvidia.com/cuda/cuda-samples/.

[13]

E. Oswald. OpenSCA, an open source toolbox for MATLAB. http://www.cs.bris.ac.uk/home/eoswald/opensca.html.

[14]

H. Patel and R. Baldwin. GPU accelerated differential power analysis. In IEEE Military Communications Conf., pages 1--5, 2012.

[15]

E. Prouff, M. Rivain, and R. Bévan. Statistical analysis of second order differential power analysis. Computers, IEEE Transactions on, 58(6):799--811, 2009.

Digital Library

[16]

O. Reparaz, B. Gierlichs, and I. Verbauwhede. Selecting time samples for multivariate dpa attacks. In Cryptographic Hardware & Embedded Systems, pages 155--174. 2012.

Digital Library

[17]

Riscure. Inspector SCA. https://www.riscure.com/security-tools/inspector-sca/.

[18]

R. Ubal, B. Jang, P. Mistry, D. Schaa, and D. Kaeli. Multi2sim: A simulation framework for cpu-gpu computing. In Proceedings of the 21st international conference on Parallel architectures and compilation techniques, pages 335--344. ACM, 2012.

Digital Library

Cited By

Jin SBettati R(2020)Reinforcement-Based Divide-and-Conquer Strategy for Side-Channel Attacks2020 IEEE International Workshop on Information Forensics and Security (WIFS)10.1109/WIFS49906.2020.9360908(1-6)Online publication date: 6-Dec-2020
https://doi.org/10.1109/WIFS49906.2020.9360908
Amaral JRegazzoni FTomas PChaves RYu TYang S(2014)Accelerating differential power analysis on heterogeneous systemsProceedings of the 9th Workshop on Embedded Systems Security10.1145/2668322.2668326(1-9)Online publication date: 12-Oct-2014
https://dl.acm.org/doi/10.1145/2668322.2668326
Gamaarachchi HRagel RJayasinghe D(2014)Accelerating correlation power analysis using graphics processing units (GPUs)7th International Conference on Information and Automation for Sustainability10.1109/ICIAFS.2014.7069547(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/ICIAFS.2014.7069547

Index Terms

Scalable and efficient implementation of correlation power analysis using graphics processing units (GPUs)

Recommendations

Accelerating a hydrological uncertainty ensemble model using graphics processing units (GPUs)

The practical application of hydrological uncertainty models that are designed to generate multiple ensembles can be severely restricted by the available computer processing power and thus, the time taken to generate the results. CPU clusters can help ...
Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms

Graphics processor units (GPU) that are originally designed for graphics rendering have emerged as massively-parallel "co-processors" to the central processing unit (CPU). Small-footprint multi-GPU workstations with hundreds of processing elements can ...
Three-dimensional thinning algorithms on graphics processing units and multicore CPUs

Three-dimensional curve skeletons are a very compact representation of three-dimensional objects with many uses and applications in ﬁelds such as computer graphics, computer vision, and medical imaging. An important problem is that the calculation of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

HASP '14: Proceedings of the Third Workshop on Hardware and Architectural Support for Security and Privacy

June 2014

89 pages

ISBN:9781450327770

DOI:10.1145/2611765

Conference Chairs:
Ruby Lee
Princeton University
,
Weidong Shi
University of Houston

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

HASP '14

Sponsor:

SIGARCH

HASP '14: The Third Workshop on Hardware and Architectural Support for Security and Privacy

June 15, 2014

Minnesota, Minneapolis, USA

Acceptance Rates

Overall Acceptance Rate 9 of 13 submissions, 69%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
363
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jin SBettati R(2020)Reinforcement-Based Divide-and-Conquer Strategy for Side-Channel Attacks2020 IEEE International Workshop on Information Forensics and Security (WIFS)10.1109/WIFS49906.2020.9360908(1-6)Online publication date: 6-Dec-2020
https://doi.org/10.1109/WIFS49906.2020.9360908
Amaral JRegazzoni FTomas PChaves RYu TYang S(2014)Accelerating differential power analysis on heterogeneous systemsProceedings of the 9th Workshop on Embedded Systems Security10.1145/2668322.2668326(1-9)Online publication date: 12-Oct-2014
https://dl.acm.org/doi/10.1145/2668322.2668326
Gamaarachchi HRagel RJayasinghe D(2014)Accelerating correlation power analysis using graphics processing units (GPUs)7th International Conference on Information and Automation for Sustainability10.1109/ICIAFS.2014.7069547(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/ICIAFS.2014.7069547

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents