Adversarial Learning for Advantage Distillation in Secret Key Agreement Over UWAC

Underwater acoustic communications (UWACs) are being adopted in several applications, and their security mechanisms require periodically renewed symmetric keys among legitimate devices. To this end, a secret key agreement (SKA) exploiting the UWAC context can be implemented. In this paper, we focus on one step of the source-based SKA protocol, i.e., the advantage distillation, when the legitimate users pre-process the channel measurement to increase the agreement while decreasing the attacker's knowledge about the key. We propose to learn the advantage distillation process by using a dataset of observed channels from the legitimate parties and the attacker, respectively. In turn, also the attacker uses such data to extract the key from its own underwater channel observations. The key extraction functions are derived through a computationally efficient data-driven adversarial strategy based on a metric related to the secret key rate that can be computed with a lower computational cost. The proposed strategy is tested considering experimental UWAC data, derived from a sea experiment. Results prove the quick convergence of the algorithm and the advantage of using the proposed techniques.