["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nThe research work addresses the need to preserve the user's privacy in urban contexts without relying on pure contemporary approaches—like k‐anonymity or stochastic noise. We present a privacy preserving methodology that adapts to varying spatial contexts of users. The methodology relies on a deep reinforcement learning approach that dynamically balances privacy versus utility. A deep reinforcement learning agent is capable of learning optimal balancing between perturbation and utility while incorporating complex factors like the user's true coordinates, privacy preferences, and the geographic context as such. Our model frames the problem as a Markov Decision Process in which an agent learns to perturb user coordinates with context‐aware Gaussian noise, governed by a reward function that simultaneously minimizes the risk of privacy leakage and maximizes the accuracy of recommendations. The agent's policy evolves through a trial‐and‐error learning process and ultimately converges to an optimal policy. The proposed method was evaluated on a dataset of restaurants across the city of Salzburg. The results demonstrate that the agent outperforms common baselines, such as adding noise sampled from a random distribution or from a distribution with fixed parameters, particularly in locations where conventional approaches encounter difficulties. The proposed method is a step forward toward the development of more trustworthy location‐based systems.\n"]