A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data
Mobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. This paper focuses on the anonymization of mobility data by obfuscating mobility heat map...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/8065 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849246508234309632 |
|---|---|
| author | Christian Dürr Gabriele S. Gühring |
| author_facet | Christian Dürr Gabriele S. Gühring |
| author_sort | Christian Dürr |
| collection | DOAJ |
| description | Mobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. This paper focuses on the anonymization of mobility data by obfuscating mobility heat maps and combining this with a local differential privacy method, which generates synthetic mobility traces. Using the San Francisco Cabspotting dataset, we compare the effectiveness of the combined approach against reidentification attacks. Our results show that mobility traces treated with both a heat map obfuscation and local differential privacy are less likely to be reidentified than those anonymized solely with Heat Map Confusion. This two-tiered anonymization process balances the trade-off between privacy and data utility, providing a robust defense against reidentification while preserving data accuracy for practical applications. The findings suggest that the integration of synthetic trace generation with heat map-based obfuscation can significantly enhance the protection of mobility data, offering a stronger solution for privacy-preserving data sharing. |
| format | Article |
| id | doaj-art-b85173b87d22420388e7c1aeb2e59532 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b85173b87d22420388e7c1aeb2e595322025-08-20T03:58:27ZengMDPI AGApplied Sciences2076-34172025-07-011514806510.3390/app15148065A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility DataChristian Dürr0Gabriele S. Gühring1Faculty Computer Sciences and Engineering, Esslingen University of Applied Sciences, Kanalstr. 33, 73728 Esslingen, GermanyFaculty Computer Sciences and Engineering, Esslingen University of Applied Sciences, Kanalstr. 33, 73728 Esslingen, GermanyMobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. This paper focuses on the anonymization of mobility data by obfuscating mobility heat maps and combining this with a local differential privacy method, which generates synthetic mobility traces. Using the San Francisco Cabspotting dataset, we compare the effectiveness of the combined approach against reidentification attacks. Our results show that mobility traces treated with both a heat map obfuscation and local differential privacy are less likely to be reidentified than those anonymized solely with Heat Map Confusion. This two-tiered anonymization process balances the trade-off between privacy and data utility, providing a robust defense against reidentification while preserving data accuracy for practical applications. The findings suggest that the integration of synthetic trace generation with heat map-based obfuscation can significantly enhance the protection of mobility data, offering a stronger solution for privacy-preserving data sharing.https://www.mdpi.com/2076-3417/15/14/8065mobility data anonymizationHeat Map ConfusionLocation Privacy Protection Mechanismreidentification attackssynthetic mobility traces |
| spellingShingle | Christian Dürr Gabriele S. Gühring A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data Applied Sciences mobility data anonymization Heat Map Confusion Location Privacy Protection Mechanism reidentification attacks synthetic mobility traces |
| title | A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data |
| title_full | A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data |
| title_fullStr | A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data |
| title_full_unstemmed | A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data |
| title_short | A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data |
| title_sort | combined approach of heat map confusion and local differential privacy for the anonymization of mobility data |
| topic | mobility data anonymization Heat Map Confusion Location Privacy Protection Mechanism reidentification attacks synthetic mobility traces |
| url | https://www.mdpi.com/2076-3417/15/14/8065 |
| work_keys_str_mv | AT christiandurr acombinedapproachofheatmapconfusionandlocaldifferentialprivacyfortheanonymizationofmobilitydata AT gabrielesguhring acombinedapproachofheatmapconfusionandlocaldifferentialprivacyfortheanonymizationofmobilitydata AT christiandurr combinedapproachofheatmapconfusionandlocaldifferentialprivacyfortheanonymizationofmobilitydata AT gabrielesguhring combinedapproachofheatmapconfusionandlocaldifferentialprivacyfortheanonymizationofmobilitydata |