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CRPs Dataset of Ring Oscillator PUF

Citation Author(s):
ENAS ABULIBDEH (System on Chip Lab, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE)
Hani Saleh (System on Chip Lab, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE)
Baker Mohammad (System on Chip Lab, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE)
Mahmoud Al-Qutayri (System on Chip Lab, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE)
Submitted by:
Enas Abulibdeh
Last updated:
DOI:
10.21227/6ssx-ft56
Data Format:
No Ratings Yet

Abstract

Physically unclonable functions (PUFs) are foundational components that offer a cost-efficient and promising solution for diverse security applications, including countering integrated circuit (IC) counterfeiting, generating secret keys, and enabling lightweight authentication. PUFs exploit semiconductor variations in ICs to derive inherent responses from imposed challenges, creating unique challenge-response pairs (CRPs) for individual devices. Analyzing PUF security is pivotal for identifying device vulnerabilities and ensuring response credibility. Consequently, CRP-centered scrutiny significantly shapes the block's resilience against general and modeling-based attacks. However, an updated and representative dataset is necessary for this analysis, as benchmark datasets evaluating PUF device efficacy and resilience are currently lacking. This study addresses this gap by producing a dataset of 300K CRPs for a digital PUF realized on a field-programmable gate array (FPGA). This dataset supplies a substantial quantity of CRPs for multi-bit responses, implicitly incorporating spatial and temporal relationships within the extracted CRPs.

Instructions:

The dataset includes two text files. The first file is the challenges and the second is the generated responses. The challenge and the response at the same offset in both files are associated and form a single CRP.

Funding Agency
Technology Innovation Institute (TII)
Grant Number
EX2021-005