CryptoGraphics: Secret Key Cryptography Using Graphics Cards

Cook, Debra L.; Ioannidis, John P. A.; Keromytis, Angelos D.; Luck, Jake

doi:10.1007/978-3-540-30574-3_23

Cited by 59 publications

(42 citation statements)

References 13 publications

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“…Therefore, encryption and decryption times of the header could be greatly reduced if only a subset of the confidential attributes is encrypted. Further reduction in the encryption and decryption times can be achieved using specialized graphic processors or external graphics cards [28]. Optimized programming and parallelization techniques can also be used to enhance the overall time performance.…”

Section: Time Performance Resultsmentioning

confidence: 99%

Providing Integrity, Authenticity, and Confidentiality for Header and Pixel Data of DICOM Images

Al-Haj

2014

J Digit Imaging

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Section: Time Performance Resultsmentioning

confidence: 99%

Providing Integrity, Authenticity, and Confidentiality for Header and Pixel Data of DICOM Images

Al-Haj

2014

J Digit Imaging

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“…GPUs have now developed into a powerful, highly parallel computing platform that finds more and more interest outside graphics-processing applications. In cryptography so far mostly secretkey applications were implemented (see, e.g., [14] and the book [15]) while taking full advantage of GPUs for public-key cryptography remained a challenge [38].…”

Section: Review Of Gpus and Gpu Programmingmentioning

confidence: 99%

ECM on Graphics Cards

Bernstein

Chen

Cheng

et al. 2009

Advances in Cryptology - EUROCRYPT 2009

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Abstract. This paper reports record-setting performance for the ellipticcurve method of integer factorization: for example, 926.11 curves/second for ECM stage 1 with B1 = 8192 for 280-bit integers on a single PC. The state-of-the-art GMP-ECM software handles 124.71 curves/second for ECM stage 1 with B1 = 8192 for 280-bit integers using all four cores of a 2.4 GHz Core 2 Quad Q6600.The extra speed takes advantage of extra hardware, specifically two NVIDIA GTX 295 graphics cards, using a new ECM implementation introduced in this paper. Our implementation uses Edwards curves, relies on new parallel addition formulas, and is carefully tuned for the highly parallel GPU architecture. On a single GTX 295 the implementation performs 41.88 million modular multiplications per second for a general 280-bit modulus. GMP-ECM, using all four cores of a Q6600, performs 13.03 million modular multiplications per second. This paper also reports speeds on other graphics processors: for example, 2414 280-bit elliptic-curve scalar multiplications per second on an older NVIDIA 8800 GTS (G80), again for a general 280-bit modulus. For comparison, the CHES 2008 paper "Exploiting the Power of GPUs for Asymmetric Cryptography" reported 1412 elliptic-curve scalar multiplications per second on the same graphics processor despite having fewer bits in the scalar (224 instead of 280), fewer bits in the modulus (224 instead of 280), and a special modulus (2 224 − 2 96 + 1).

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“…Many attempts have been made to use graphics processors for security applications, including cryptography [11,8], data carving [17], and intrusion detection [14,30,10,27,13]. In our previous work [30], we extended Snort to offload the string matching operations of the Snort IDS to the GPU, offering a three times speedup to the processing throughput compared to a CPU-only implementation.…”

Section: Related Workmentioning

confidence: 99%

Regular Expression Matching on Graphics Hardware for Intrusion Detection

Vasiliadis

Polychronakis

Antonatos

et al. 2009

Lecture Notes in Computer Science

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Abstract. The expressive power of regular expressions has been often exploited in network intrusion detection systems, virus scanners, and spam filtering applications. However, the flexible pattern matching functionality of regular expressions in these systems comes with significant overheads in terms of both memory and CPU cycles, since every byte of the inspected input needs to be processed and compared against a large set of regular expressions. In this paper we present the design, implementation and evaluation of a regular expression matching engine running on graphics processing units (GPUs). The significant spare computational power and data parallelism capabilities of modern GPUs permits the efficient matching of multiple inputs at the same time against a large set of regular expressions. Our evaluation shows that regular expression matching on graphics hardware can result to a 48 times speedup over traditional CPU implementations and up to 16 Gbit/s in processing throughput. We demonstrate the feasibility of GPU regular expression matching by implementing it in the popular Snort intrusion detection system, which results to a 60% increase in the packet processing throughput.

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CryptoGraphics: Secret Key Cryptography Using Graphics Cards

Cited by 59 publications

References 13 publications

Providing Integrity, Authenticity, and Confidentiality for Header and Pixel Data of DICOM Images

Providing Integrity, Authenticity, and Confidentiality for Header and Pixel Data of DICOM Images

ECM on Graphics Cards

Regular Expression Matching on Graphics Hardware for Intrusion Detection

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