The usage of mobile devices has become ubiquitous in today’s world. More people are adopting smartphones to be able to connect with family and friends, easily perform day-to-day tasks, discover new facets, etc. The smartphones are location-enabled and because of this, the rise of Location-Based Applications (LBAs) in the last decade has been phenomenal. Different LBAs use location information for advertising, recommendations, finding new friends, suggesting a new point of interests, etc., based on user trends. However, sharing location data can reveal personality traits, illnesses, political inclinations and religious views. When the location data is collected for a certain period, whereabouts can be easily guessed. For instance, once the location information is collected for a few days, the arrival and departure time from “work” location can be easily estimated. Thus, the characteristics revealed just by location details are narrowly perceived by most users and imposes a privacy risk. To address this privacy risk, a software agent is developed which helps to estimate and visualize this threat. It can push notifications and aware the user about the privacy risk before he/she decides to share the location with LBAs. In this thesis, we present an algorithm that predicts the user’s future movements with confidence percentages. This algorithm processes the raw location coordinates and extracts the meaningful locations. Based on the meaningful locations, a prediction model is formed. This model is used to predict future visits based on the user’s current location. The algorithm is evaluated using real-life data from Microsoft Geolife dataset. To inform the user about the privacy threat, a visualization of future visits with confidence percentages is implemented. Finally, a prototype on an Android device is developed to help user estimate the privacy risk before sharing location information on LBAs.