A systematic review of data privacy in Mobility as a Service (MaaS)
Bibliographic record
Abstract
Mobility as a Service (MaaS) integrates various transportation modes to offer seamless urban mobility solutions. However, the extensive collection and sharing of user data on MaaS platforms pose significant privacy challenges. This systematic review identifies key data privacy concerns, evaluates current privacy-preserving technologies, and explores the role of regulatory frameworks in ensuring user privacy in MaaS systems. Using the PRISMA framework, a comprehensive literature search across Web of Science, Elsevier, and IEEE Xplore databases resulted in the selection of 32 studies for detailed analysis. The review is structured around three main themes: (1) Privacy-Preserving Techniques, including anonymization strategies (k-anonymity, differential privacy, obfuscation), encryption methods (blockchain, cryptographic protocols), federated learning for decentralized data processing, and advanced algorithms for optimizing privacy budgets and balancing utility-privacy trade-offs; (2) User Trust and Privacy Perceptions, highlighting that trust in service providers is essential for MaaS adoption, privacy concerns may impact adoption but do not necessarily prevent it (the “privacy paradox”), and awareness of data misuse affects user trust and willingness to adopt MaaS; and (3) Regulatory Frameworks, focusing on the importance of GDPR compliance to ensure strict data protection through consent and transparency, and embedding privacy-by-design principles within MaaS architectures to safeguard user data from the outset. This review emphasizes the need for a holistic approach, integrating technological innovation, user-centered design, and strong regulatory oversight to effectively address privacy challenges in MaaS. Future research should focus on developing scalable privacy frameworks that protect user data without compromising operational efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".