Hardware Authentication Mechanism for Consortium Blockchain Based on Differentiated Physical Unclonable Function Models

doi:10.3969/j.issn.1671-1122.2025.11.007

Abstract

Abstract:

In decentralized systems involving hardware data acquisition, hardware-level device authentication, trusted computing and data traceability with multi-party verification are critical to ensuring end-to-end data security. As a secure and cost-effective hardware security primitive, physical unclonable function (PUF) has been adopted for device authentication and data verification. However, traditional PUF verification schemes are prone to leakage in multi-node environments, leading to authentication failure. To address this risk, a novel scheme that distributed differentiated and re-configurable PUF models to verification nodes for device authentication was proposed in this work. Taking hardware supply chain as the representative scenario, dedicated hardware module design, authentication protocols, and smart contract implementation were performed. Experimental results on the Hyperledger Fabric demonstrate that our approach maintaines system authentication efficiency while significantly enhancing robustness against verification data leakage. Furthermore, it effectively mitigates machine learning-based modeling attacks targeting PUF.

Key words: consortium blockchain, physical unclonable function, device authentication, modeling attack

CLC Number:

TP309

SHEN Haoting, PENG Zhigang, LIU Yuxuan, WANG Yafei. Hardware Authentication Mechanism for Consortium Blockchain Based on Differentiated Physical Unclonable Function Models[J]. Netinfo Security, 2025, 25(11): 1732-1744.

Figures/Tables 12

References 30

[1]	POWER D. Supply Chain Management Integration and Implementation: A Literature Review[J]. Supply Chain Management: An International Journal, 2005, 10(4): 252-263. doi: 10.1108/13598540510612721 URL
[2]	MALIK P K, SHARMA R, SINGH R, et al. Industrial Internet of Things and Its Applications in Industry 4.0: State of the Art[J]. Computer Communications, 2021, 166: 125-139. doi: 10.1016/j.comcom.2020.11.016 URL
[3]	ELKINS P, BAKER T. Carbon Taxes and Carbon Emissions Trading[J]. Journal of Economic Surveys, 2001, 15(3): 325-376. doi: 10.1111/joes.2001.15.issue-3 URL
[4]	CHRISTIDIS K, DEVETSIKIOTIS M. Blockchains and Smart Contracts for the Internet of Things[J]. IEEE Access, 2016, 4: 2292-2303. doi: 10.1109/ACCESS.2016.2566339 URL
[5]	LI Zhetao, KANG Jiawen, YU Rong, et al. Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things[J]. IEEE Transactions on Industrial Informatics, 2017, 14(8): 3690-3700.
[6]	DIJESH P, BABU S S, VIJAYALAKSHMI Y. Enhancement of E-Commerce Security through Asymmetric Key Algorithm[J]. Computer Communications, 2020, 153: 125-134. doi: 10.1016/j.comcom.2020.01.033 URL
[7]	JAIN A K, GUPTA N, GUPTA B B. A Survey on Scalable Consensus Algorithms for Blockchain Technology[EB/OL]. (2024-07-08)[2025-07-20]. https://doi.org/10.1016/j.csa.2024.100065.
[8]	XU Qiang, ZHENG Rong, SAAD W, et al. Device Fingerprinting in Wireless Networks: Challenges and Opportunities[J]. IEEE Communications Surveys & Tutorials, 2015, 18(1): 94-104.
[9]	OSBORN J D, CHALLENER D C. Trusted Platform Module Evolution[J]. Johns Hopkins APL Technical Digest (Applied Physics Laboratory), 2013, 32(2): 536-543.
[10]	HERDER C, YU M D, KOUSHANFAR F, et al. Physical Unclonable Functions and Applications: A Tutorial[J]. Proceedings of the IEEE, 2014, 102(8): 1126-1141. doi: 10.1109/JPROC.2014.2320516 URL
[11]	ZHANG Jiliang, SHEN Chaoqun. Set-Based Obfuscation for Strong PUFs against Machine Learning Attacks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, 68(1): 288-300. doi: 10.1109/TCSI.8919 URL
[12]	SUH G E, DEVADAS S. Physical Unclonable Functions for Device Authentication and Secret Key Generation[C]// ACM. 2007 44th ACM/IEEE Design Automation Conference. New York: ACM, 2007: 9-14.
[13]	RÜHRMAIR U, SEHNKE F, SÖLTER J, et al. Modeling Attacks on Physical Unclonable Functions[C]// ACM. The 17th ACM Conference on Computer and Communications Security. New York: ACM, 2010: 237-249.
[14]	DU Wenliang, ZHENG Honghao, WON K. SEED Emulator: An Internet Emulator for Research and Education[C]// ACM. The 21st ACM Workshop on Hot Topics in Networks (HotNets). New York: ACM, 2022: 101-107.
[15]	ANDROULAKI E, BARGER A, BORTNIKOV V, et al. Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains[C]// ACM. The 13th ACM European Conference on Computer Systems (EuroSys). New York: ACM, 2018: 1-15.
[16]	GASSEND B, CLARKE D, VAN D M, et al. Silicon Physical Random Functions[C]// ACM. The 9th ACM Conference on Computer and Communications Security. New York: ACM, 2002: 148-160.
[17]	PAPPU R, RECHT B, TAYLOR J, et al. Physical One-Way Functions[J]. Science, 2002, 297(5589): 2026-2030. pmid: 12242435
[18]	HALAK B, ZWOLINSKI M, MISPAN M S. Overview of PUF-Based Hardware Security Solutions for the Internet of Things[C]// IEEE. 2016 IEEE 59th International Midwest Symposium on Circuits and Systems. New York: IEEE, 2016: 1-4.
[19]	DAS B P, AMRUTUR B, JAMADAGNI H S, et al. Within-Die Gate Delay Variability Measurement Using Reconfigurable Ring Oscillator[J]. IEEE Transactions on Semiconductor Manufacturing, 2009, 22(2): 256-267. doi: 10.1109/TSM.2009.2017662 URL
[20]	LIM D, LEE J W, GASSEND B, et al. Extracting Secret Keys from Integrated Circuits[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2005, 13(10): 1200-1205. doi: 10.1109/TVLSI.2005.859470 URL
[21]	NGUYEN P H, SAHOO D P, JIN Chenglu, et al. The Interpose PUF: Secure PUF Design against State-of-the-Art Machine Learning Attacks[J]. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019, 2019(4): 243-290.
[22]	ISLAM M N, KUNDU S. Enabling IC Traceability via Blockchain Pegged to Embedded PUF[J]. ACM Transactions on Design Automation of Electronic Systems, 2019, 24(3): 1-23.
[23]	ASIF R, GHANEM K, IRVINE J. Proof-of-PUF Enabled Blockchain: Concurrent Data and Device Security for Internet-of-Energy[J]. Sensors, 2020, 21(1): 1-32. doi: 10.3390/s21010001 URL
[24]	LI Dawei, CHEN Ruonan, LIU Di, et al. Blockchain-Based Authentication for IIoT Devices with PUF[EB/OL]. (2022-07-07)[2025-07-25]. https://doi.org/10.1016/j.sysarc.2022.102638.
[25]	ROSTAMI M, MAJZOOBI M, KOUSHANFAR F, et al. Robust and Reverse-Engineering Resilient PUF Authentication and Key-Exchange by Substring Matching[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(1): 37-49. doi: 10.1109/TETC.6245516 URL
[26]	TUN N W, MAMBO M. Secure PUF-Based Authentication Systems[EB/OL]. (2024-08-15)[2025-07-25]. https://doi.org/10.3390/s24165295.
[27]	MALL P, AMIN R, DAS A K, et al. PUF-Based Authentication and Key Agreement Protocols for IoT, WSNs, and Smart Grids: A Comprehensive Survey[J]. IEEE Internet of Things Journal, 2022, 9(11): 8205-8228. doi: 10.1109/JIOT.2022.3142084 URL
[28]	GASSEND B, DIJK M V, CLARKE D, et al. Controlled Physical Random Functions and Applications[J]. ACM Transactions on Information and System Security (TISSEC), 2008, 10(4): 1-22.
[29]	MAJZOOBI M, ROSTAMI M, KOUSHANFAR F, et al. Slender PUF Protocol:A Lightweight, Robust, and Secure Authentication by Substring Matching[C]// IEEE. 2012 IEEE Symposium on Security and Privacy Workshops. New York: IEEE, 2012: 33-44.
[30]	YAN Wei, TEHRANIPOOR F, CHANDY J A. PUF-Based Fuzzy Authentication without Error Correcting Codes[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, 36(9): 1445-1457. doi: 10.1109/TCAD.2016.2638445 URL

DDC-(x, y)-PUF类型	训练所用CRPs数量/对	预测准确率	训练时间/s
DDC-(1,1)-PUF	4000	76.15%	4.22
	5600	87.01%	9.93
	16000	98.05%	21.42
DDC-(2,2)-PUF	4800	78.23%	5.13
	8000	89.12%	10.10
	32000	97.45%	48.77
DDC-(3,3)-PUF	64000	78.16%	72.57
	128000	89.57%	148.09
	288000	97.34%	437.01
DDC-(4,4)-PUF	120000	75.83%	142.23
	160000	87.82%	423.11
	400000	96.02%	899.12