Efficient interleaved Montgomery modular multiplication for lattice-based cryptography

Akleylek, Sedat; Tok, Zaliha Yüce

doi:10.1587/elex.11.20140960

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Off-chip memory refers to global memory, which is essentially the DRAM. It comes in a large size (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), but the access latency can be up to 300× slower than the registers.…”

Section: A Overview Of Gpu Architecture and Cuda Programming Modelmentioning

confidence: 99%

TensorCrypto: High Throughput Acceleration of Lattice-Based Cryptography Using Tensor Core on GPU

Lee

Seo

Zhang³

et al. 2022

IEEE Access

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Tensor core is a newly introduced hardware unit in NVIDIA GPU chips that allows matrix multiplication to be computed much faster than in the integer and floating-point units. In this paper, we show that for the first time, tensor core can be used to accelerate state-of-the-art lattice-based cryptosystems. We employed tensor core to speed up polynomial convolution, which is the most time consuming operation in lattice-based cryptosystems. Towards that aim, several parallel algorithms are proposed to allow the tensor core to handle flexible matrix sizes and ephemeral key pairs. Experimental results show that the polynomial convolution computed using the tensor core is at least 2× faster than the version implemented with conventional integer units of the NVIDIA GPU. The proposed tensorcore-based polynomial convolution technique was applied to NTRU, one of the finalists in NIST postquantum cryptography (PQC) standardization. It achieved 2.02×/1.98× (encapsulation) and 1.56×/1.90× (decapsulation) higher throughput on two parameter sets (ntruhps2048509 and ntruhps2048677), compared to the conventional integer-based implementations on a GPU. In particular, the proposed implementation techniques achieved throughput up to 793651 key encapsulations per second and 505051 decapsulations per second on a RTX2060 GPU. To demonstrate the flexibility of the proposed technique, we extend the implementation to other lattice-based cryptosystems that have a small modulus: LAC and two variant parameter sets in FrodoKEM. Considering that the IoT gateway devices and cloud servers need to handle massive connections from the sensor nodes, the proposed high throughput implementation on GPU is very useful in securing the IoT communication.

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Section: A Overview Of Gpu Architecture and Cuda Programming Modelmentioning

confidence: 99%

TensorCrypto: High Throughput Acceleration of Lattice-Based Cryptography Using Tensor Core on GPU

Lee

Seo

Zhang³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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On the Efficiency of Polynomial Multiplication for Lattice-Based Cryptography on GPUs Using CUDA

Akleylek

Dagdelen

Tok³

2016

Cryptography and Information Security in the Balkans

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Sparse polynomial multiplication for lattice-based cryptography with small complexity

2015

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Efficient interleaved Montgomery modular multiplication for lattice-based cryptography

Cited by 6 publications

References 8 publications

TensorCrypto: High Throughput Acceleration of Lattice-Based Cryptography Using Tensor Core on GPU

TensorCrypto: High Throughput Acceleration of Lattice-Based Cryptography Using Tensor Core on GPU

On the Efficiency of Polynomial Multiplication for Lattice-Based Cryptography on GPUs Using CUDA

Sparse polynomial multiplication for lattice-based cryptography with small complexity

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