Inferring Activities and Profiles of Users Based on Trajectory Leakage in Mobile Ad Network

With the widespread use of smartphones and the development of ad networks, mobile in-app targeted ads have become more and more prevalent, leveraging users' geolocation for targeting purposes. This service involves a large amount of user location data, which may not only expose sensitive locations closely associated with the users, but also reveal the users' activities and profiles. Previous studies have utilized various machine learning methods to infer users' activities or predict their future activities based on the location data from location-based social networks (LBSNs). These approaches, however, often require large datasets for training and are also resource-intensive. Unlike active behaviors, such as checking in, where users intentionally record their location, location data are passively recorded by mobile apps in the background, making inferring activities more challenging. Considering the rapid progress in the reasoning abilities of the large language models (LLMs) in recent years, we aim to evaluate user's activity and profile leakage through LLMs with the assistance of map APIs. We conduct the experiment on the location dataset, which is generated according to specified profiles. The results of the experiment show that the LLM can infer users' activities with an accuracy rate scoring up to 96.1 %, and there is also a high probability of predicting the users' profiles, such as the occupation.