# On the Use of CSI for the Generation of RF Fingerprints and Secret Keys

arXiv: https://arxiv.org/abs/2110.15415 · HTML: https://mlsanity.com/paper/2110.15415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Kernel-based Tests for Joint Independence](https://mlsanity.com/paper/1603.00285/) (2016)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends](https://mlsanity.com/paper/1505.07919/) (2015)
- [Improving Channel Charting with Representation-Constrained Autoencoders](https://mlsanity.com/paper/1908.02878/) (2019)
- [Siamese Neural Networks for Wireless Positioning and Channel Charting](https://mlsanity.com/paper/1909.13355/) (2019)
- [Learning-Aided Physical Layer Authentication as an Intelligent Process](https://mlsanity.com/paper/1808.02456/) (2018)
- [On the Security of Key Extraction from Measuring Physical Quantities](https://mlsanity.com/paper/1311.4591/) (2013)
- [The Passive Eavesdropper Affects my Channel: Secret-Key Rates under Real-World Conditions (Extended Version)](https://mlsanity.com/paper/1701.03904/) (2017)
- [Authenticated Secret Key Generation in Delay Constrained Wireless Systems](https://mlsanity.com/paper/2001.07162/) (2020)

## Abstract (arXiv)

This paper presents a systematic approach to use channel state information for authentication and secret key distillation for physical layer security (PLS). We use popular machine learning (ML) methods and signal processing-based approaches to disentangle the large scale fading and be used as a source of uniqueness, from the small scale fading, to be treated as a source of shared entropy secret key generation (SKG). The ML-based approaches are completely unsupervised and hence avoid exhaustive measurement campaigns. We also propose using the Hilbert Schmidt independence criterion (HSIC); our simulation results demonstrate that the extracted stochastic part of the channel state information (CSI) vectors are statistically independent.