Blockchain-Enabled Secure VLSI Framework For Distributed Hardware Verification And IP Protection

Authors

  • Dr. V. T. Krishnaprasath Associate Professor Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore, Tamil Nadu, India
  • Dr. T. Surya Assistant Professor Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Kinathukadavu, Coimbatore – 642109, Tamil Nadu, India
  • B. Suganthi Associate Professor – ECE RVS College of Engineering and Technology Coimbatore – 641402, Tamil Nadu, India
  • Mohammed Kasim M Assistant Professor (SG) Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology Coimbatore – 641105, Tamil Nadu, India
  • Priya B Assistant Professor Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore, Tamil Nadu, India
  • P. Priyadharsini Assistant Professor Department of Information Technology Nehru Institute of Engineering and Technology Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.29284/ijasis.12.1.2026.187-198

Keywords:

Blockchain, Secure VLSI, Hardware Verification, IP Protection, Hardware Provenance, Smart Contracts, Permissioned Ledger, Distributed Design Flow, Tamper-Evident Audit, Anti-Counterfeiting, Trojan Resistance, Design Integrity, Secure EDA.

Abstract

The globalization of semiconductor supply chains and the rise of third-party IP reuse have intensified concerns around hardware Trojan insertion, counterfeit IP distribution, unauthorized overbuilding, and dispute-prone verification workflows in modern VLSI design. This paper proposes a Blockchain-Enabled Secure VLSI Framework that unifies distributed hardware verification, provenance tracking, and IP protection through tamper-evident ledger records and cryptographically verifiable design artifacts. The proposed framework registers RTL/netlist milestones, verification reports, test signatures, and PDK-dependent constraints as immutable transactions, enabling all stakeholders (IP vendors, integrators, foundries, and verification labs) to validate authenticity and integrity without exposing sensitive design content. To prevent IP leakage, the framework supports hash-anchored commitments, permissioned access control, and zero-knowledge–ready attestations for key verification claims (e.g., “coverage ≥ threshold” or “equivalence passed”) while keeping raw waveforms and proprietary constraints off-chain. A smart-contract policy engine enforces licensing (time-bound/feature-bound), audit logging, and revocation, while a lightweight on-chain/off-chain storage strategy ensures scalability. Analytical evaluation and prototype-level profiling indicate that the approach can provide end-to-end traceability with sub-second block confirmation in permissioned mode, ~25–45% reduction in dispute resolution time via automated audit trails, and ~15–30% lower manual compliance effort by standardizing verification evidence exchange. The framework is suitable for secure SoC integration, multi-vendor verification, and IP lifecycle governance in advanced VLSI flows.

References

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, Aug. 2002.

[10] R. V. Kulkarni and G. K. Venayagamoorthy, "Bio-inspired algorithms for autonomous deployment and localization of sensor nodes," IEEE Trans. Systems, Man, and Cybernetics, vol. 40, no. 6, pp. 663–675, 2010.

[11] A. Bari, A. Jaekel, and S. Bandyopadhyay, "Clustering strategies for improving energy efficiency in wireless sensor networks," Computer Communications, vol. 30, no. 14–15, pp. 2828–2841, 2007.

[12] S. Hussain and O. Islam, "Fault-tolerant clustering in wireless sensor networks," Proc. IEEE Int. Conf. Distributed Computing Systems Workshops, pp. 9–14, 2007.

[13] J. N. Al-Karaki and A. E. Kamal, "Routing techniques in wireless sensor networks: A survey," IEEE Wireless Communications, vol. 11, no. 6, pp. 6–28, 2004.

[14] A. Abbasi and M. Younis, "A survey on clustering algorithms for wireless sensor networks," Computer Communications, vol. 30, no. 14–15, pp. 2826–2841, 2007.

[15] S. A. Sert, A. Yazici, and A. Cosar, "Energy-efficient clustering and routing in wireless sensor networks using swarm intelligence," IEEE Systems Journal, vol. 13, no. 3, pp. 1–12, 2019.

[16] Sood, K., Dhanaraj, R.K., Balusamy, B., Grima, S. and Uma Maheshwari, R. eds., 2022. Big Data: A game changer for insurance industry. Emerald Publishing Limited.

[17] Janarthanan, R., Maheshwari, R.U., Shukla, P.K., Shukla, P.K., Mirjalili, S. and Kumar, M., 2021. Intelligent detection of the PV faults based on artificial neural network and type 2 fuzzy systems. Energies, 14(20), p.6584.

[18] Maheshwari, R.U., Kumarganesh, S., KVM, S., Gopalakrishnan, A., Selvi, K., Paulchamy, B., Rishabavarthani, P., Sagayam, K.M., Pandey, B.K. and Pandey, D., 2025. Advanced plasmonic resonance-enhanced biosensor for comprehensive real-time detection and analysis of deepfake content. Plasmonics, 20(4), pp.1859-1876.

[19] Appalaraju, M., Sivaraman, A.K., Vincent, R., Ilakiyaselvan, N., Rajesh, M. and Maheshwari, U., 2021. Machine learning-based categorization of brain tumor using image processing. In Artificial Intelligence and Technologies: Select Proceedings of ICRTAC-AIT 2020 (pp. 233-242). Singapore: Springer Singapore.

[2] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor networks," Proc. 33rd Annual Hawaii Int. Conf. System Sciences, pp. 1–10, 2000.

[20] Maheshwari, R.U., B.Paulchamy, Pandey, B.K. et al. Enhancing Sensing and Imaging Capabilities Through Surface Plasmon Resonance for Deepfake Image Detection. Plasmonics (2024). https://doi.org/10.1007/s11468-024-02492-1

[3] O. Younis and S. Fahmy, "HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks," IEEE Trans. Mobile Computing, vol. 3, no. 4, pp. 366–379, 2004.

[4] S. Lindsey and C. Raghavendra, "PEGASIS: Power-efficient gathering in sensor information systems," Proc. IEEE Aerospace Conf., pp. 1125–1130, 2002.

[5] J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948, 1995.

[6] M. Dorigo and T. Stützle, "Ant Colony Optimization," MIT Press, Cambridge, 2004.

[7] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical Report, Erciyes University, Turkey, 2005.

[8] X. S. Yang, "Firefly algorithms for multimodal optimization," Proc. Int. Symp. Stochastic Algorithms, pp. 169–178, 2009.

[9] S. Selvakennedy, S. Sinnappan, and Y. Shang, "A biologically-inspired clustering protocol for wireless sensor networks," Computer Communications, vol. 30, no. 14–15, pp. 2786–2801, 2007.

Downloads

Published

2026-01-15

Issue

Section

Articles

How to Cite

[1]
D. V. T. Krishnaprasath, D. T. Surya, B. Suganthi, M. Kasim M, P. B, and P. Priyadharsini, “Blockchain-Enabled Secure VLSI Framework For Distributed Hardware Verification And IP Protection”, IJASIS, pp. 187–198, Jan. 2026, doi: 10.29284/ijasis.12.1.2026.187-198.

Share