# Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT device

arXiv: https://arxiv.org/abs/2210.01749 · HTML: https://mlsanity.com/paper/2210.01749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 85 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [Resource Management in Fog/Edge Computing: A Survey](https://mlsanity.com/paper/1810.00305/) (2018)
- [MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants](https://mlsanity.com/paper/2207.09744/) (2022)
- [A New Security Boundary of Component Differentially Challenged XOR PUFs Against Machine Learning Modeling Attacks](https://mlsanity.com/paper/2206.01314/) (2022)

## Abstract (arXiv)

Physical Unclonable Functions (PUFs) are promising security primitives for resource-constrained IoT devices. And the XOR Arbiter PUF (XOR-PUF) is one of the most studied PUFs, out of an effort to improve the resistance against machine learning attacks of probably the most lightweight delay-based PUFs - the Arbiter PUFs. However, recent attack studies reveal that even XOR-PUFs with large XOR sizes are still not safe against machine learning attacks. Increasing PUF stages or components and using different challenges for different components are two ways to improve the security of APUF-based PUFs, but more stages or components lead to more hardware cost and higher operation power, and different challenges for different components require the transmission of more bits during operations, which also leads to higher power consumption. In this paper, we present a strategy that combines the choice of XOR Arbiter PUF (XOR-PUF) architecture parameters with the way XOR-PUFs are used to achieve lightweights in hardware cost and energy consumption as well as security against machine learning attacks. Experimental evaluations show that with the proposed strategy, highly lightweight component-differentially challenged XOR-PUFs can withstand the most powerful machine learning attacks developed so far and maintain excellent intra-device and inter-device performance, rendering this strategy a potential blueprint for the fabrication and use of XOR-PUFs for resource-constrained IoT applications.