Quality of Service Guarantees for Physical Unclonable Functions
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Abstract (arXiv)
We consider a secret key agreement problem in which noisy physical unclonable function (PUF) outputs facilitate reliable, secure, and private key agreement with the help of public, noiseless, and authenticated storage. PUF outputs are highly correlated, so transform coding methods have been combined with scalar quantizers to extract uncorrelated bit sequences with reliability guarantees. For PUF circuits with continuous-valued outputs, the models for transformed outputs are made more realistic by replacing the fitted distributions with corresponding truncated ones. The state-of-the-art PUF methods that provide reliability guarantees to each extracted bit are shown to be inadequate to guarantee the same reliability level for all PUF outputs. Thus, a quality of service parameter is introduced to control the percentage of PUF outputs for which a target reliability level can be guaranteed. A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs. Furthermore, we conversely show that it is not possible to provide such reliability guarantees without eliminating any PUF output if no extra secrecy and privacy leakage is allowed.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 38 references that are in the map. First posted on arXiv 12 Jul 2021.
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6 of 38 references.
References · 6
- 2017Code Constructions for Physical Unclonable Functions and Biometric Secrecy Systemscited by 2
- 2019Private Authentication with Physical Identifiers Through Broadcast Channel Measurementscited by 2
- 2020Secure and Reliable Key Agreement with Physical Unclonable Functionscited by 2
- 2020Low-complexity and Reliable Transforms for Physical Unclonable Functionscited by 2
- 2020Randomized Nested Polar Subcode Constructions for Privacy, Secrecy, and Storagecited by 1
- 2020Explainable Machine Learning based Transform Coding for High Efficiency Intra Predictioncited by 1
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- 2026Every Bit Counts: A Theoretical Study of Precision-Expressivity Tradeoffs in Quantized Transformers
- 2026The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis
- 2025Attack on a PUF-based Secure Binary Neural Network
- 2026High-Rate Public-Key Pseudorandom Codes for Edit Errors ↗
- 2025A Systematic Evaluation of On-Device LLMs: Quantization, Performance, and Resources