Predominant in today society, mobile apps are rising as promising application systems for automatic control. An app can be viewed as a plant, processing input signals (queries, phone data, etc.) and generating outputs (such as a service or an answer). Guaranteeing that the app complies with a desired behavior is a major safety challenge. This work focuses on privacy issues for geolocated mobile apps. Many applications use the location data to provide a service (e.g., navigation, fitness) or to improve it (e.g., weather forecast, social media). This gain in service utility comes at the cost of personal data sharing. Such threat to user privacy can be leveraged by protection mechanisms, e.g., addition of noise to the location data. However, state-of-the-art techniques still lack means of ensuring both data utility and privacy in a dynamics utilization context. This paper presents the first non-linear analytical modeling followed by a control formulation for regulating the privacy level in a mobile app. The privacy is sensed using the well established notion of Point of Interest. Through modeling, we highlight the control challenges, namely the non-linearity and time-variance of the plant, its high sensibility to noise and the impact of the user's mobility pattern-seen a disturbance. A controller is designed, combining feedback with anticipation action. Evaluation is performed using mobility records from two real-world multi-users datasets. Our approach enables, with a unique and universal tuning, to robustly meet privacy objectives with preserved utility and negligible computational overhead. Control algorithm, experimental evaluation and analysis scripts are available online for reproducibility.