Demo Data from VLDB 2026 / reviewers in the wild / expert
Xusheng Qin
dblp:386/8318
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis › post-quantum cryptography › code-based key encapsulation
BIKE |
0.8 | 1 | 2024 | 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 |
0.8 | 1 | 2024 | ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024 |
Cryptographic primitives and cryptanalysis
post-quantum cryptography |
0.8 | 1 | 2024 | 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 |
0.8 | 1 | 2024 | ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU Acceleration · IEEE Trans. Inf. Forensics Secur. 2024 |
GPUs and heterogeneous computing
GPU computing |
0.8 | 1 | 2024 | 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 |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ECO-BIKE: Bridging the Gap Between PQC BIKE and GPU AccelerationabstractAdvancements 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 |