As location-based services (LBS) have grown in popularity, the collection of human mobility data has become increasingly extensive to build machine learning (ML) models offering enhanced convenience to LBS 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.

为了更好地理解和量化基于移动数据的机器学习模型的隐私泄露情况，我们设计了一个隐私攻击套件，其中包含数据提取和成员推断攻击，针对最广泛使用的基于兴趣点推荐模型之一进行定制。我们的实证评估使用了两个真实世界的移动数据集，证明了当前的兴趣点推荐模型容易受到我们的攻击。我们还提供了独特的发现，以了解哪种类型的移动数据更容易受到隐私攻击。最后，我们评估了对抗这些攻击的防御方法，并强调了未来的方向和挑战。

你去了哪里？地点推荐隐私风险研究