Joint Secrecy-Privacy Resource Allocation for UARIS-Assisted Underwater Communications Using Reinforcement Learning

Underwater acoustic communication (UAC) is of great strategic importance for marine resource exploration and security collaboration. However, its open physical nature exposes communication links to severe eavesdropping and localization threats, while limited bandwidth and severe attenuation further exacerbate the difficulty of secure transmission. To address this, this study introduces the underwater acoustic reconfigurable intelligent surface (UARIS) to reconfigure acoustic propagation paths, leveraging its programmable reflection capability to enhance link quality and provide additional spatial degrees of freedom for location privacy protection. Accounting for the partial observability caused by the coarse observations of a mobile eavesdropping user (EU), noisy channel state information (CSI), and the practical constraint of UARIS discrete phase quantization, a utility maximization problem is formulated to jointly optimize the secrecy rate and location privacy. To tackle the strong non-convexity and coupled constraints in dynamic environments, a Gated Recurrent and Conformal-calibrated Soft Actor–Critic (GC-SAC) algorithm is proposed. Specifically, GC-SAC employs a gated recurrent unit (GRU) to capture the temporal statistical features of channel evolution. By integrating a risk prediction network with a conformal calibration mechanism, conservative estimation and robust regulation of multidimensional constraint risks are enhanced. Simulation results demonstrate that the GC-SAC algorithm achieves faster convergence and superior stability in dynamic underwater environments. Compared with representative baselines, the proposed algorithm exhibits significant advantages in secrecy rate and location privacy protection, validating its effectiveness for UARIS-assisted secure resource optimization in underwater scenarios.