Business Value of Privacy-Preserving Mobility Analytics in Platform Operations: A Federated Risk-Utility Framework

Authors

  • Jiahui Ding 1 https://orcid.org/0009-0003-2530-7375
    • Conceptualization
    • Formal Analysis
    • Investigation
    • Methodology
    • Visualization
    • Writing – Original Draft Preparation
  • Fafa (Monica) Yan 2,* https://orcid.org/0000-0002-2033-1099
    • Data Curation
    • Supervision
    • Validation
    • Writing – Review & Editing
1 School of Business, Beijing Technology and Business University, Beijing, China 100048
2 School of Economics and Management, Guangzhou Institute of Science and Technology, Guangzhou, China 510540
Corresponding author: yanff@mail3.sysu.edu.cn

DOI:

https://doi.org/10.63646/BPUL9289

Keywords:

Mobility analytics; platform operations; federated learning; privacy preserving analytics; risk utility framework; business value; urban trajectory mining; data governance

Abstract

Mobility platforms use location and movement information to forecast demand, allocate capacity, and improve service, but the same information can expose sensitive patterns about individuals. Federated analytics can reduce the need to pool raw data, although model updates, participation records, and released outputs may still leak information. This article develops a risk–utility framework for evaluating the business value of privacy-preserving mobility analytics in platform operations. The framework links a defined operational decision to expected service gains, deployment costs, residual incident losses, and separately enforced privacy, security, and service constraints. It distinguishes centralized data pooling, federated learning, and a federated design with secure aggregation and a properly specified differential privacy mechanism. A hypothetical monthly decision example compares the alternatives using stated assumptions: their respective expected incremental values are 104, 112, and 106 thousand US dollars, making ordinary federation the highest-valued option in the baseline scenario. The ranking changes when the severity of modeled incidents increases, and privacy requirements may independently exclude an option. A separate one-release Gaussian mechanism illustrates how a formal user-level privacy statement depends on the unit of protection, clipping, the fixed denominator, and the trust boundary; it does not establish a privacy guarantee for multi-round federated training. The calculations are analytical illustrations rather than results from an implemented system or observed platform data. The article concludes with an evaluation protocol addressing causal business impact, heterogeneous participants, fairness, privacy accounting, and deployment governance.

How to Cite

Ding, J., & Yan, F. (Monica). (2026). Business Value of Privacy-Preserving Mobility Analytics in Platform Operations: A Federated Risk-Utility Framework. Journal of Business and Data Analytics, 4(9), 129-148. https://doi.org/10.63646/BPUL9289

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