VLIA: Navigating Shadows with Proximity for Highly Accurate Visited Location Inference Attack against Federated Recommendation Models
Thirasara Ariyarathna, Meisam Mohammady, Hye-Young Paik, Salil S. Kanhere · 2024
Personalized location recommendation allows users to enjoy a seamless travel experience by suggesting the optimal travel locations/routes based on user preferences. Most service providers collect users' location data centrally to develop accurate route recommendation applications. Federated learning (FL) can be used as an inherent privacy-preserving mechanism in these applications to prevent users from sharing private data. However, recent research shows that FL is still vulnerable to privacy leakages. Therefore, many FL-based recommendation systems use Local Differential Privacy (LDP) to defend against such attacks. In this paper, we propose the Visited Location Inference Attack (VLIA), a novel attack for federated location recommendation systems through the lens of Membership Inference Attack (MIA). Specifically, we focus on inferring user behaviour data (visited locations) even when the federated recommendation system is protected with LDP. We design and implement VLIA leveraging both embedding and proximity information of locations, making the inference more accurate. Our extensive experiments with two state-of-the-art personalized route recommendation (PRR) systems implemented in the FL setting and two real-world trajectory datasets showcase the effectiveness of the VLIA attack. Our results show that LDP cannot defend VLIA unless the recommendation performance is significantly compromised.