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Efficient Implementation of Lightweight Hash Functions on GPU and Quantum Computers for IoT Applications
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DOI：10.1109/ACCESS.2022.3179970.png)
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Abstract
En 中文
Secure communication is important for Internet of Things (IoT) applications, to avoid cyber-security attacks. One of the key security aspects is data integrity, which can be protected by employing cryptographic hash functions. Recently, US National Institute of Standards and Technology (NIST) announced a competition to standardize lightweight hash functions, which can be used in IoT applications. IoT communication involves various hardware platforms, from low-end microcontrollers to high-end cloud servers with GPU accelerators. Since many sensor nodes are connected to the gateway devices and cloud servers, performing high throughput integrity check is important to secure IoT applications. However, this is a time consuming task even for high-end servers, which may affect the response time in IoT systems. Moreover, no prior work had evaluated the performance of NIST candidates on contemporary processors like GPU and quantum computers. In this study, we showed that with carefully crafted implementation techniques, all the finalist hash function candidates in the NIST standardization competition can achieve high throughput (up-to 1,000 Gbps) on a RTX 3080 GPU. This research output can be used by IoT gateway devices and cloud servers to perform data integrity checks at high speed, thus ensuring a timely response. In addition, this is also the first study that showcase the implementation of NIST lightweight hash functions on a quantum computer (ProjectQ). Besides securing the communication in IoT, these efficient implementations on a GPU and quantum computer can be used to evaluate the strength of respective hash functions against brute-force attack.
Keywords:
Internet of Things
Hash functions
Graphics processing units
Computers
Logic gates
Quantum computing
NIST
Graphics processing units (GPU)
hash function
lightweight cryptography
quantum computer
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
