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Xusheng Qin

dblp:386/8318 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cryptographic primitives and cryptanalysis · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis › post-quantum cryptography › code-based key encapsulation
BIKE
0.812024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024
Cryptographic primitives and cryptanalysis › post-quantum cryptography
code-based key encapsulation
0.812024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024
Cryptographic primitives and cryptanalysis
post-quantum cryptography
0.812024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024
GPUs and heterogeneous computing › GPU computing
cryptographic acceleration
0.812024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024
GPUs and heterogeneous computing
GPU computing
0.812024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024
Cryptographic primitives and cryptanalysis › finite field arithmetic
polynomial multiplication
0.212024
ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024

Methods — techniques the papers use, named apart from their topics

parallel dense polynomial multiplication · 1.5XOR optimization · 1.53-threading parallel architecture · 1.5
YearPublicationVenuePosition
2024 ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration
abstract
Advancements in quantum computing pose a threat to public-key cryptosystems, leading to the development of post-quantum cryptography. NIST is standardizing candidate algorithms, with BIKE, a code-based key encapsulation mechanism, among those under consideration. Performance is crucial in NIST PQC standardization process, and researchers have introduced a range of optimization techniques for BIKE across various platforms. To the best of our knowledge, our Efficient CryptOgraphy BIKE (ECO-BIKE) represents the first attempt at optimizing the implementation of BIKE on GPU architecture. In this paper, we introduce a comprehensive construction of a 3-threading parallel architecture tailored for the BIKE cryptosystem. This architecture covers a range of computational tasks, addressing operations from low-level to high-level computations. These include a parallel dense polynomial multiplication scheme with a better memory access pattern and a better XOR calculation, which forms the basis for a comprehensive parallel execution framework for the entire BIKE algorithm. Targeted optimizations are implemented for specific modules (KEYGEN, ENCAPS, DECAPS), which collectively enhance the overall efficiency of the algorithm. Our ECO-BIKE exhibits exceptional throughput performance on the NVIDIA GeForce RTX 4090. In the 3-thread mode, the throughput of the KEYGEN, ENCAPS, and DECAPS modules reaches 24.033 kops/s, 277.789 kops/s, and 5.817 kops/s, respectively. Our proposed optimal parallel multiplication scheme achieves a significantly higher overall throughput of 481.302 kops/s. These results highlight the substantial computational advantages our approach provides for cryptographic workloads.
Jiankuo Dong, Yusheng Fu, Xusheng Qin, Zhenjiang Dong, Fu Xiao 0001, Jingqiang Lin 0001
IEEE Trans. Inf. Forensics Secur.3