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# Characterizing User Exposure in Mobility Data: Insights from Varying User Behavioral Profiles

Lucas Félix, Anne Josiane Kouam, Aline Carneiro Viana, Nadjib Achir, Jussara M Almeida

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Lucas Félix, Anne Josiane Kouam, Aline Carneiro Viana, Nadjib Achir, Jussara M Almeida. Characterizing User Exposure in Mobility Data: Insights from Varying User Behavioral Profiles. NetMob 2025 - 9th edition of NetMob, Oct 2025, Paris, France. hal-05288506

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Submitted on 29 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Characterizing User Exposure in Mobility Data: Insights from Varying User Behavioral Profiles

LUCAS FÉLIX<fnref n="1" />,<fnref n="2" />, ANNE J. KOUAM<fnref n="1" />, ALINE C. VIANA<fnref n="1" />, NADJIB ACHIR<fnref n="1" />, AND JUSSARA M. ALMEIDA<fnref n="2" />

## 1. INTRODUCTION

The increasing availability of large-scale mobility datasets presents new opportunities to analyze and model human movement at unprecedented scales. However, such data can also compromise user privacy by revealing sensitive personal information (e.g., home location). Studies have shown that just a few spatiotemporal points in a user's trajectory can be enough to uniquely identify them within large datasets [1]. This has spurred growing research on quantifying user exposure—the risk of distinguishing an individual from others—in mobility data [2]. A widely used approach is the *Uniqueness measure* [1], which relates exposure to the unicity in the subsequences of locations visited by the user (i.e., points in the user's trajectory).

Recently, we proposed MoBES—Mobility Behavior-based user Exposure Score—which evaluates exposure based on how distinct a user's mobility behavior is from that of others in the dataset [2]. Unlike Uniqueness, which relies on raw trajectory data, MoBES employs a set of mobility metrics to capture multiple complementary facets of mobility behavior, enabling the identification of users with highly distinctive patterns and, consequently, greater exposure risk.

Previous analyses of Uniqueness and MoBES have focused solely on population-level results. However, user mobility behavior is highly heterogeneous and can be categorized into distinct profiles [3]. This raises an open research question: *Which types of users, based on their mobility profiles, are more prone to exposure in mobility datasets?* To our knowledge, neither Uniqueness nor MoBES has been examined through the lens of mobility profiles, a research gap that we address next.

## 2. METHODOLOGY

We discuss next how we cluster users into mobility profiles, and present the two techniques employed to quantify user exposure.

**User mobility profiling:** We adopt the proposal by Amichi et al. [3] to categorize users into three commonly observed mobility profiles, namely: (i) *Routiners*, or users who mostly follow routine movements (e.g., home-work) with occasional novelty; (ii) *Scouters*, who frequently explore new areas, showing high diversity and low regularity; and (iii) *Regulars*, who exhibit a balanced mix of routine and exploration.

To assign a profile to a user, we follow a two-step methodology. First, each visited location in the user's trajectory is classified based on visit frequency as either an exploration (new or rarely visited place) or a return (frequently visited place). From these classifications, we compute two metrics: intermittency (how often users alternate between exploration and return) and degree of return (how frequently users revisit a return location after an exploration). Second, users are clustered based on these metrics using a Gaussian Mixture Model (GMM), with three clusters. We assess cluster quality using the Silhouette Score.

**Quantifying user exposure:** We here quantify the exposure of a user using Uniqueness [1] and MoBES [2]. Uniqueness assesses user exposure by counting the number of unique time-ordered

(sub)sequences of locations in a user's trajectory, given an aggregation time window $t_w$. A Uniqueness equal to 1 indicates the user has unique (sub)sequences, being thus exposed.

MoBES, in turn, quantifies a user's exposure by measuring the distance to their k-nearest neighbors in a multidimensional behavioral space, where each dimension represents a different mobility metric [2]. The underlying assumption is that users who are farther from their nearest neighbors exhibit more distinctive mobility behaviors and are thus more exposed.

Formally, given a set $\mathcal{U}$ of users and a set $\mathcal{M}$ of $m$ mobility metrics, each user $i \in \mathcal{U}$ is represented by a vector $\mathcal{M}_i = \{m_i^1, m_i^2, \dots, m_i^m\} \in \mathbb{R}^+$, where $m_i^j$ corresponds to the value of metric $m_j$ computed from user $i$'s mobility trajectory. Thus vector $\mathcal{M}_i$ places user $i$ as a point in an $m$-dimensional behavioral space. To compute the exposure of a user $i$, we first calculate the distance from $i$ to each other user $j \in \mathcal{U}$ as an average relative absolute difference across all metrics, i.e., as

$$D_{i,j} = \frac{1}{m} \sum_{r=1}^{m} \left| \frac{m_r^1 - m_i^1}{m_r^1} \right| \quad \text{Given the set } \mathcal{N} \text{ of the k nearest neighbors of } i \text{ according to the defined distance function, we then compute the MoBES value for user } i \text{ as the average distance from } i \text{ to its k-nearest neighbors, i.e., } \text{MoBES}_i = \frac{\sum_{j \in \mathcal{N}} \mathcal{N}_j}{k} D_{i,j}.$$

MoBES provides each user with a continuous exposure score. Depending on the context, multiple thresholds can be applied to define different exposure categories. Here, we adopt a binary classification by using a single threshold $\tau$: a user $i$ is considered exposed if $\text{MoBES}_i > \tau$; otherwise, $i$ is deemed protected due to behavioral similarity with their neighbors.

We emphasize that Uniqueness and MoBES capture user exposure from complementary perspectives. While the former explores only the raw trajectory data, the latter leverages mobility metrics to encapsulate processed insights about how users occupy space over time, thus capturing multiple facets of user mobility behavior. In this work, we set $t_w = 4$ for the time window used by Uniqueness, and instantiate MoBES with $k = 10$, $\tau = 0.05$ and a set $\mathcal{M}$ of $m = 13$ metrics, widely used in the mobility literature. The selected metrics are: Radius of Gyration (RoG), 2k Radius of Gyration (2KRoG), Maximum Distance between Consecutive Locations (MDIST), Number of Visits (NOV), Average and Standard Deviation of Jump Lengths (AJL/SJL), and Number of Unique Locations (NUL), Entropy (ENT), Stationarity (STAT) and Regularity (REG), Average and Standard Deviation of Waiting Time (AWT/SWT). We refer to [2] for a detailed description of these metrics as well as parameter choice.

## 3. EXPERIMENTAL EVALUATION

**Datasets:** We assess user exposure using two anonymized cellular datasets from the metropolitan areas of Shanghai and Shenzhen, China. The Shanghai dataset provides near-hourly user locations, while the Shenzhen dataset is event-based, recording locations only when users make calls or send SMS. These datasets differ significantly in properties and mobility patterns; full details on preprocessing and characterization can be found in [4].

<footnote n="1">¹INRIA, France</footnote>

<footnote n="2">²Universidade Federal de Minas Gerais, Brazil</footnote>

<footnote>*lucasgsfelix@ufmg.com</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Distributions of MoBES and Uniqueness values in Shanghai and Shenzhen datasets</figcaption></figure>

In brief, the Shanghai dataset includes 58,502 users tracked over 10 days, with an average of 180.37 records and 12.4 unique locations per user. The Shenzhen dataset covers 31,859 users over 15 days, averaging 136.01 records and 9.67 unique locations—indicating more limited mobility in both distinct places and total visitations. User clustering based on mobility profiles [3] shows that in Shanghai, 22.2% are routiners, 56.4% regulars, and 21.4% scouters. In Shenzhen, the distribution shifts to 31.4% routiners, 44.5% regulars, and 24.1% scouters. The higher proportion of routiners in Shenzhen reflects the more limited observation area, with mobility often confined to a few locations (e.g., home and work). The higher fraction of Scouters in Shenzhen, in turn, is due to its event-based collection, which increases data sparsity, recording users in more varied locations.

**User exposure:** Figure 1 shows the distributions of MoBES and Uniqueness scores across mobility profiles for both datasets.

In Shanghai, Uniqueness yields similar exposure levels across profiles, with most users fully exposed (Uniqueness equal to 1), likely due to: (i) the larger geographic coverage reduces overlap in users' visited areas, increasing the likelihood of capturing unique movements; (ii) frequent location updates (nearly hourly) enhance the detection of individual variations in movement and stationarity. In contrast, MoBES shows clear differences across profiles: nearly 20% of routiners have behaviors very similar to their neighbors, with MoBES values close to zero. Those are overly stationary users, who mostly stay on the same location (e.g., home), being thus "protected" by the behavioral similarity. Scouters and regulars, in turn, display higher MoBES scores, with scouters being slightly more exposed due to greater behavioral variability across multiple mobility metrics.

In Shenzhen, the profiles have distinct Uniqueness distributions: scouters tend to be the most unique, consistent with their higher mobility and greater diversity of visited locations, followed by regulars and routiners. MoBES, in turn, has the same distribution across profiles, despite their distinct mobility patterns. An analysis of the contribution of each metric for MoBES computation reveals also great similarities, with 2k Radius of Gyration, Entropy, and Number of Unique Locations being the most important ones for all profiles. These contrasting results between MoBES and Uniqueness likely reflect intrinsic differences in mobility patterns of both cities, as captured by each dataset.

As argued in [2], regardless of parameterization (e.g., $\tau$ value), the sets of users considered exposed according to Uniqueness and MoBES do exhibit important differences, which corroborates that both techniques capture complementary notions of exposure. Due to space constraints, we here focus on the users flagged as exposed by MoBES, using $\tau = 0.05$. In such setup, we find that roughly 82% routiners, 95% regulars, and 98% scouters in Shanghai as well as 90% routiners, 90% regulars, and 90% scouters in Shenzhen have MoBES value greater than 0.05, being thus considered exposed.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Average user behavior in Shanghai (user exposure according to MoBES with $\tau = 0.05$).</figcaption></figure>

For illustration, Figure 2 presents radar charts showing the normalized average values of mobility metrics for exposed and non-exposed routiners and scouters (according to MoBES) in Shanghai. Clearly, routiners deemed as exposed tend to exhibit more exploratory behavior, with greater diversity/entropy and lower stationary. Conversely, exposed scouters tend to exhibit subtler differences but across more metrics, also related to more exploration, notably lower stationarity, higher entropy as well as longer maximum distance between consecutive locations.

## 4. CONCLUSION

In conclusion, user behavior may significantly affect exposure, whether considering spatial-temporal patterns or mobility behavior. Yet, this effect is strongly influenced by dataset characteristics. The differences observed for MoBES and Uniqueness in both Shanghai and Shenzhen highlight the complementarity of these measures and their potential for combined use in exposure evaluation. Additionally, mobility profiles can help guide personalized protection strategies, enabling more targeted privacy measures while preserving data utility.

**Acknowledgments:** This work is part of the Mob Sci-Dat Factory project (ANR-23-PEMO-0004) under the France 2030 program. It was partially supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and by the CAPES-STIC-AMSUD 22-STIC-07 LINT project.

## REFERENCES

1. Y.-A. De Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel, Sci. Reports (2013).

2. L. Félix, A. J. Kouam, A. C. Viana, et al., "The silent signature: Behavior-based user exposure in mobility data," in Proc. IEEE MDM, (2025).

3. L. Amichi, A. C. Viana, M. Crovella, and A. Loureiro, "Unders. individuals' proclivity for novelty seeking," in Proc. ACM SIGSPATIAL, (2020).

4. J. P. Esper, A. C. Viana, and J. M. Almeida, "Beauty or beast: Human behavioral insights and learning power of federated mobility prediction," in Proc. ACM SIGSPATIAL, (2024).