High Throughput Implementation of Post-Quantum Key Encapsulation and Decapsulation on GPU for Internet of Things Applications

Lee, Wai-Kong; Hwang, Seong Oun

doi:10.1109/tsc.2021.3103956

Cited by 14 publications

(15 citation statements)

References 28 publications

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“…In this paper, we propose a novel dynamic indexing pattern to allow the level combination to be done on more levels. On a RTX2060 GPU, our GPU-accelerated NTT with length n = 4096 can calculate 73590 NTTs per second, which is 1.39× faster than the technique proposed by Lee et al [LH21] 4. We propose a privacy-preserving SVM classification technique using RLWE-IPFE scheme as a suitable use case to validate the performance of the proposed cuFE.…”

Section: Introductionmentioning

confidence: 88%

“…Hence, a combination of fine-grain and coarse-grain parallelism could be useful for certain applications to achieve a balanced latency and throughput performance. A notable example can be found in [LH21], where the authors proposed to compute one Kyber [BDK + 18] key-encapsulation mechanism (KEM) per GPU block (fine-grain), and utilize many parallel blocks (coarse-grain) to execute many different instantiations.…”

Section: Gpu Programming Modelmentioning

confidence: 99%

“…Some previous research works were performed to investigate the efficient ways to implement NTT on a GPU. Recently, Lee et al [LH21] presented the NTT implementation on GPUs for Kyber KEM [BDK + 18]. They proposed to combine the first two levels of NTT so that more computations can be performed on the registers, effectively reducing the accesses to shared memory.…”

Section: Related Workmentioning

confidence: 99%

“…Number theoretic transform (NTT) is another computation intensive operation in the RLWE-IPFE [MKMS22]. Recently, Lee et al [LH21] demonstrated an efficient technique to compute parallel NTT by combining the first two levels. However, their technique employed a static indexing pattern, which cannot be extended to cover more NTT levels.…”

Section: Introductionmentioning

confidence: 99%

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cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Han

Lee

Karmakar

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users' privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve huge amount of data. In this paper, we present a high performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24×, 40.02×, 156.30× and 18.76× speed-up for Setup, Encrypt, KeyGen and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12× faster than the AVX2 version that takes 23 seconds.

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Section: Introductionmentioning

confidence: 88%

Section: Gpu Programming Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Han

Lee

Karmakar

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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“…Quantum computing is finding its applications in a variety of technological areas ranging from navigation [203] to channel coding [204] and IoT [205]. Quantum-inspired AI is a recent research trend dealing with the applications of quantum computing in artificial intelligence.…”

Section: Quantum-inspired Aimentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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