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A Persistent Hierarchical Bloom Filter-based Framework for Authentication and Tracking of ICs

Fairuz Shadmani Shishir, Md Mashfiq Rizvee, Tanvir Hossain, Tamzidul Hoque, Domenic Forte, Sumaiya Shomaji

TL;DR

This study introduces the Persistent Hierarchical Bloom Filter framework, ensuring swift and accurate IC authentication with an accuracy rate of 100% across the supply chain even with noisy PUF-generated signatures.

Abstract

Detecting counterfeit integrated circuits (ICs) in unreliable supply chains demands robust tracking and authentication. Physical Unclonable Functions (PUFs) offer unique IC identifiers, but noise undermines their utility. This study introduces the Persistent Hierarchical Bloom Filter (PHBF) framework, ensuring swift and accurate IC authentication with an accuracy rate of 100% across the supply chain even with noisy PUF-generated signatures.

A Persistent Hierarchical Bloom Filter-based Framework for Authentication and Tracking of ICs

TL;DR

This study introduces the Persistent Hierarchical Bloom Filter framework, ensuring swift and accurate IC authentication with an accuracy rate of 100% across the supply chain even with noisy PUF-generated signatures.

Abstract

Detecting counterfeit integrated circuits (ICs) in unreliable supply chains demands robust tracking and authentication. Physical Unclonable Functions (PUFs) offer unique IC identifiers, but noise undermines their utility. This study introduces the Persistent Hierarchical Bloom Filter (PHBF) framework, ensuring swift and accurate IC authentication with an accuracy rate of 100% across the supply chain even with noisy PUF-generated signatures.
Paper Structure (5 sections, 4 equations, 2 figures, 1 table)

This paper contains 5 sections, 4 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. methodology
  3. Results
  4. Evaluation Metric
  5. Conclusion

Figures (2)

  • Figure 1: A schematic of the proposed PHBF framework
  • Figure 2: Performance evaluation of PHBF using ROC curve with four query datasets: (a) dataset containing real noise (75° C), (b) dataset containing real noise (100° C), (c) dataset containing synthetic noise (clustered), and (d) dataset containing synthetic noise (uniform).