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Collection, usage and privacy of mobility data in the enterprise and public administrations

Alexandra Kapp

TL;DR

The paper addresses privacy-utility trade-offs in enterprise and public administration mobility data under GDPR. It uses 13 expert interviews across German organizations to map data sources, analysis tasks, and privacy practices, revealing broad variation in privacy adoption and limited deployment of state-of-the-art methods like differential privacy. It highlights the need for practice-oriented privacy frameworks, standardized similarity measures, and accessible tools to bridge research and industry. Overall, it provides groundwork for standardized evaluations of privacy-enhancing approaches and informs policy and tool development for real-world mobility data use.

Abstract

Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a trade-off arises as such techniques potentially decrease the utility of the data and limit its use. While much research on anonymization techniques exists, there is little information on the actual implementations by practitioners, especially outside the big tech context. Within our study, we conducted expert interviews to gain insights into practices in the field. We categorize purposes, data sources, analysis, and modeling tasks to provide a profound understanding of the context such data is used in. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy. We provide groundwork for further research on practice-oriented research by identifying privacy needs of practitioners and extracting relevant mobility characteristics for future standardized evaluations of privacy-enhancing methods.

Collection, usage and privacy of mobility data in the enterprise and public administrations

TL;DR

The paper addresses privacy-utility trade-offs in enterprise and public administration mobility data under GDPR. It uses 13 expert interviews across German organizations to map data sources, analysis tasks, and privacy practices, revealing broad variation in privacy adoption and limited deployment of state-of-the-art methods like differential privacy. It highlights the need for practice-oriented privacy frameworks, standardized similarity measures, and accessible tools to bridge research and industry. Overall, it provides groundwork for standardized evaluations of privacy-enhancing approaches and informs policy and tool development for real-world mobility data use.

Abstract

Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a trade-off arises as such techniques potentially decrease the utility of the data and limit its use. While much research on anonymization techniques exists, there is little information on the actual implementations by practitioners, especially outside the big tech context. Within our study, we conducted expert interviews to gain insights into practices in the field. We categorize purposes, data sources, analysis, and modeling tasks to provide a profound understanding of the context such data is used in. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy. We provide groundwork for further research on practice-oriented research by identifying privacy needs of practitioners and extracting relevant mobility characteristics for future standardized evaluations of privacy-enhancing methods.
Paper Structure (21 sections, 1 figure, 5 tables)

This paper contains 21 sections, 1 figure, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Methodology
  4. Participant recruitment and moderation
  5. Analysis and coding
  6. Research ethics and anonymization
  7. Limitations
  8. Findings
  9. Purposes for data collection and analyses
  10. Data sources and responsibilities
  11. Data analysis and modeling techniques
  12. Privacy
  13. Practical implications
  14. Privacy needs of practitioners
  15. Similarity measures
  16. ...and 6 more sections

Figures (1)

  • Figure 1: Schematic illustration of the context that privacy-enhanced methods are used in. Data sources are used (in a privacy enhanced manner) for data analysis or modeling tasks to achieve a certain purpose. The evaluation of privacy enhancements is pursued by similarity measures which quantify the difference between analyses conducted with privacy-enhanced (black solid arrow) and raw (orange dashed arrow) data input. Note, that for simplicity the use of privacy-enhanced methods is placed between the data source and the data analysis, which is only one possible set-up.