Accelerating RSA with Fine-Grained Parallelism Using GPU

Yang, Yang; Guan, Zhi; Sun, Huiping; Chen, Zhong

doi:10.1007/978-3-319-17533-1_31

Cited by 19 publications

(8 citation statements)

References 14 publications

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“…Emmart and Weems [7] applied one thread to a row oriented multiplication for 1024-bit modular exponentiation and a distributed model based on CIOS method for 2048bit modular exponentiation. Yang [24] used an Integrated Operand Scanning (IOS) method with single limb or two limbs for Montgomery multiplication algorithm to implement RSA-1024/2048 decryption.…”

Section: Related Workmentioning

confidence: 99%

“…Note that RSA-1024 decryption of [22] has latency of about 150 ms, while 2048bit RSA decryption of ours reaches 21.52 ms when it reaches the throughput peak. Yang [24] reported the latency for RSA-2048 throughput of 5,244 (scaled as 31,782) is 195.27 ms (scaled as 225.60 ms). The throughput of the proposed RSA-2048 implementation is 132% performance of Yang's work, but the latency is 9.4% of their work [24].…”

Section: Proposed Versusmentioning

confidence: 99%

“…Yang [24] reported the latency for RSA-2048 throughput of 5,244 (scaled as 31,782) is 195.27 ms (scaled as 225.60 ms). The throughput of the proposed RSA-2048 implementation is 132% performance of Yang's work, but the latency is 9.4% of their work [24]. Our peak RSA-2048 throughput is slightly slower than the fastest integer-based implementation [7], while our latency is 3 times faster.…”

Section: Proposed Versusmentioning

confidence: 99%

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Utilizing the Double-Precision Floating-Point Computing Power of GPUs for RSA Acceleration

Dong

Zheng

Pan

et al. 2017

Security and Communication Networks

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Asymmetric cryptographic algorithm (e.g., RSA and Elliptic Curve Cryptography) implementations on Graphics Processing Units (GPUs) have been researched for over a decade. The basic idea of most previous contributions is exploiting the highly parallel GPU architecture and porting the integer-based algorithms from general-purpose CPUs to GPUs, to offer high performance. However, the great potential cryptographic computing power of GPUs, especially by the more powerful floating-point instructions, has not been comprehensively investigated in fact. In this paper, we fully exploit the floating-point computing power of GPUs, by various designs, including the floating-point-based Montgomery multiplication/exponentiation algorithm and Chinese Remainder Theorem (CRT) implementation in GPU. And for practical usage of the proposed algorithm, a new method is performed to convert the input/output between octet strings and floating-point numbers, fully utilizing GPUs and further promoting the overall performance by about 5%. The performance of RSA-2048/3072/4096 decryption on NVIDIA GeForce GTX TITAN reaches 42,211/12,151/5,790 operations per second, respectively, which achieves 13 times the performance of the previous fastest floating-point-based implementation (published in Eurocrypt 2009). The RSA-4096 decryption precedes the existing fastest integer-based result by 23%.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Versusmentioning

confidence: 99%

Section: Proposed Versusmentioning

confidence: 99%

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Utilizing the Double-Precision Floating-Point Computing Power of GPUs for RSA Acceleration

Dong

Zheng

Pan

et al. 2017

Security and Communication Networks

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“…With specialized hardware that incorporates many custom modular exponentiation modules, overall execution time can be reduced significantly. Similarly, a GPU implementation is reported [82] to reach the peak throughtput [20]. On the other hand, our solution requires 449 ms for the same authentication setup.…”

Section: Complexity Analysis Of the Proposed Systemmentioning

confidence: 75%

“…On the other hand, one advantage of the proposed system from the time complexity point of view is that majority of the expensive operations (i.e., mainly modular exponentiations) can be performed in parallel. Therefore, custom ASIC [81] or GPU implementations [82] can accelerate protocol considerably. For instance, the custom modular exponentiation circuit for RSA in [81] reports 0.89 ms execution time on a circuit of 153,000 equivalent gate counts.…”

Section: Complexity Analysis Of the Proposed Systemmentioning

confidence: 99%

THRIVE: threshold homomorphic encryption based secure and privacy preserving biometric verification system

Karabat

Kiraz

Erdoğan

et al. 2015

EURASIP J. Adv. Signal Process.

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In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user's private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.

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