Secrets Everywhere: Auditing Memorization in Mobility Prediction Models
2026-08-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied how models that predict where people will go next might accidentally remember and expose sensitive travel details from the data they were trained on. They found this is tricky because human movement happens on many scales, like individual places or routes, making privacy risks complex. To better understand this, the authors created a way to measure how much these models memorize different parts of a person's trips. Their tests showed that the models often remember regular users' patterns, which could lead to privacy leaks. They suggest privacy checks should be a standard part of developing these prediction systems.
human mobility predictionmemorizationprivacy riskstrajectory datatraining datamulti-scale analysisuser behaviordata extractionprivacy auditinginference time
Authors
Anne Josiane Kouam, Hristo Boyadzhiev, Konrad Rieck
Abstract
Human mobility prediction models, which forecast the next location in a user's trajectory, are increasingly deployed in urban analytics, navigation, and personalized services. Yet, little is known about their potential to memorize and expose sensitive user trajectories from training data. While memorization has been extensively studied in language models, mobility prediction poses unique challenges: training sequences encode human behavior at various spatial and temporal scales, creating privacy risks at different granularities. In this paper, we conduct the first systematic audit of memorization in mobility prediction models. While prior work has shown that privacy leaks can arise from such models, we systematically assess and quantify memorization risks at scale. We identify key challenges, including the lack of a randomness space, the multi-scale structure of trajectories, and user-specific behavioral diversity. To address these challenges, we introduce a framework to quantify mobility memorization at different levels of granularity: individual locations, anchor pairs, and subtrajectory segments. We also develop user-grounded reference sets to assess how likely a model is to prefer training data over realistic alternatives. Our evaluation across multiple models and datasets reveals pervasive memorization patterns that correlate with user regularity and increase the risk of data extraction at inference time. Our findings call for mandatory privacy auditing in mobility prediction models.