Tracing the unseen: a deep dive into mobility behavior exposure
- 1Universidade Federal de Minas Gerais
- 2INRIA
Abstract
Mobility data provides rich insights into human behavior, enabling applications in transportation, retail, and marketing, among others. However, it also poses significant privacy risks, as the unicity of individual mobility patterns may enable user identification within the dataset. Existing measures that aim to quantify the degree of unicity in users’ characteristics, here referred to as user exposure, primarily focus on spatio-temporal unicity (e.g., Uniqueness), while overlooking broader behavioral traits that may also increase identification risks. To address this limitation, we have proposed MoBES (Mobility Behavior-based Exposure Score), a method for quantifying user exposure in mobility datasets. MoBES models user mobility behavior in a multidimensional behavioral space and estimates exposure based on behavioral distance. It also provides a continuous value score, enabling a more refined, nuanced analysis of user exposure. Building on our preliminary study of MoBES, this work considerably broadens and deepens the analysis of its design considerations for user exposure based on mobility behavior. To this end, we evaluate MoBES on two distinct Call Detail Record (CDR) datasets and analyze its behavior in a privacy-preserving setting where location data is perturbed using Differential Privacy (DP). Using two datasets enables a comprehensive comparative analysis, offering deeper insight into how behavioral patterns influence exposure levels. Our findings show that, although MoBES often aligns with traditional spatio-temporal measures, it also reveals some potential behavioral vulnerabilities that such measures fail to capture. Furthermore, we find that privacy protection mechanisms can effectively reduce MoBES exposure scores; however, the extent of this reduction varies across behavioral dimensions. This variation reveals that even under privacy protection, certain metrics could still be explored by potential adversarial methods.
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BibTeX
@article{felix2026tracing,
title = {{Tracing the unseen: a deep dive into mobility behavior exposure}},
author = {Lucas G. S. Félix and Anne Josiane Kouam and Aline Carneiro Viana and Nadjib Achir and Jussara M. Almeida},
journal = {EPJ Data Science},
year = {2026},
month = jul,
doi = {10.1140/epjds/s13688-026-00680-z},
url = {https://doi.org/10.1140/epjds/s13688-026-00680-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - Tracing the unseen: a deep dive into mobility behavior exposure
AU - Lucas G. S. Félix
AU - Anne Josiane Kouam
AU - Aline Carneiro Viana
AU - Nadjib Achir
AU - Jussara M. Almeida
PY - 2026
DA - 2026/07/01
JO - EPJ Data Science
DO - 10.1140/epjds/s13688-026-00680-z
UR - https://doi.org/10.1140/epjds/s13688-026-00680-z
AB - Mobility data provides rich insights into human behavior, enabling applications in transportation, retail, and marketing, among others. However, it also poses significant privacy risks, as the unicity of individual mobility patterns may enable user identification within the dataset. Existing measures that aim to quantify the degree of unicity in users’ characteristics, here referred to as user exposure, primarily focus on spatio-temporal unicity (e.g., Uniqueness), while overlooking broader behavioral traits that may also increase identification risks. To address this limitation, we have proposed MoBES (Mobility Behavior-based Exposure Score), a method for quantifying user exposure in mobility datasets. MoBES models user mobility behavior in a multidimensional behavioral space and estimates exposure based on behavioral distance. It also provides a continuous value score, enabling a more refined, nuanced analysis of user exposure. Building on our preliminary study of MoBES, this work considerably broadens and deepens the analysis of its design considerations for user exposure based on mobility behavior. To this end, we evaluate MoBES on two distinct Call Detail Record (CDR) datasets and analyze its behavior in a privacy-preserving setting where location data is perturbed using Differential Privacy (DP). Using two datasets enables a comprehensive comparative analysis, offering deeper insight into how behavioral patterns influence exposure levels. Our findings show that, although MoBES often aligns with traditional spatio-temporal measures, it also reveals some potential behavioral vulnerabilities that such measures fail to capture. Furthermore, we find that privacy protection mechanisms can effectively reduce MoBES exposure scores; however, the extent of this reduction varies across behavioral dimensions. This variation reveals that even under privacy protection, certain metrics could still be explored by potential adversarial methods.
SN - 2193-1127
ER -