  As location-based services (LBS) have grown in popularity, more human
mobility data has been collected. The collected data can be used to build
machine learning (ML) models for LBS to enhance their performance and improve
overall experience for users. However, the convenience comes with the risk of
privacy leakage since this type of data might contain sensitive information
related to user identities, such as home/work locations. Prior work focuses on
protecting mobility data privacy during transmission or prior to release,
lacking the privacy risk evaluation of mobility data-based ML models. To better
understand and quantify the privacy leakage in mobility data-based ML models,
we design a privacy attack suite containing data extraction and membership
inference attacks tailored for point-of-interest (POI) recommendation models,
one of the most widely used mobility data-based ML models. These attacks in our
attack suite assume different adversary knowledge and aim to extract different
types of sensitive information from mobility data, providing a holistic privacy
risk assessment for POI recommendation models. Our experimental evaluation
using two real-world mobility datasets demonstrates that current POI
recommendation models are vulnerable to our attacks. We also present unique
findings to understand what types of mobility data are more susceptible to
privacy attacks. Finally, we evaluate defenses against these attacks and
highlight future directions and challenges. Our attack suite is released at
https://github.com/KunlinChoi/POIPrivacy.
