My home is my secret: concealing sensitive locations by context-aware trajectory truncation

Brauer, Anna; Mäkinen, Ville; Forsch, Axel; Oksanen, Juha; Haunert, Jan‐Henrik

doi:10.1080/13658816.2022.2081694

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…We use the Site-sensitive Trajectory Truncation (S-TT) mechanism proposed in Brauer et al (2022) to suppress location measurements around sensitive locations. So far, the service does not offer the possibility to indicate sensitive locations manually.…”

Section: Protection Of Sensitive Locationsmentioning

confidence: 99%

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Brauer¹,

Mäkinen²,

Oksanen³

2023

AGILE GIScience Ser.

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Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.

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Section: Protection Of Sensitive Locationsmentioning

confidence: 99%

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Brauer¹,

Mäkinen²,

Oksanen³

2023

AGILE GIScience Ser.

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Efficient Mining of Volunteered Trajectory Datasets

Forsch,

Funke,

Haunert

et al. 2023

Volunteered Geographic Information

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With the ubiquity of mobile devices that are capable of tracking positions (be it via GPS or Wi-Fi/mobile network localization), there is a continuous stream of location data being generated every second. These location measurements are typically not considered individually but rather as sequences, each of which reflects the movement of one person or vehicle, which we call trajectory. This chapter presents new algorithmic approaches to process and visualize trajectories both in the network-constrained and the unconstrained case.

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Understanding the protection of privacy when counting subway travelers through anonymization

Shafaeipour,

Stanciu,

van Steen

et al. 2024

Computers, Environment and Urban Systems

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My home is my secret: concealing sensitive locations by context-aware trajectory truncation

Cited by 6 publications

References 44 publications

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Efficient Mining of Volunteered Trajectory Datasets

Understanding the protection of privacy when counting subway travelers through anonymization

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