KNEW: Key Generation using NEural Networks from Wireless Channels

Secret keys can be generated from reciprocal channels to be used for shared secret key encryption. However, challenges arise in practical scenarios from non-reciprocal measurements of reciprocal channels due to changing channel conditions, hardware inaccuracies and estimation errors resulting in low key generation rate (KGR) and high key disagreement rates (KDR). To combat these practical issues, we propose KNEW Key Generation using NEural Networks from Wireless Channels, which extracts the implicit features of channel in a compressed form to derive keys with high agreement rate. Two Neural Networks (NNs) are trained simultaneously to map each other's channel estimates to a different domain, the latent space, which remains inaccessible to adversaries. The model also minimizes the distance between the latent spaces generated by two trusted pair of nodes, thus improving the KDR. Our simulated results demonstrate that the latent vectors of the legitimate parties are highly correlated yielding high KGR (≈ 64 bits per measurement) and low KDR (<0.05 in most cases). Our experiments with over-the-air signals show that the model can adapt to realistic channels and hardware inaccuracies, yielding over 32 bits of key per channel estimation without any mismatch.