Beyond privacy regulations: an ethical approach to data usage in transportation

TL;DR

This paper explores how Federated Machine Learning can ethically and effectively enable privacy-preserving data analysis in the transportation industry, surpassing mere regulatory compliance.

Contribution

It introduces the application of Federated Learning in transportation, outlining new use-cases and a product lifecycle that emphasizes ethical data usage beyond regulations.

Findings

Federated Learning enables privacy-sensitive data processing in transportation.

It supports personalized services without compromising user privacy.

A new product lifecycle for ethical data usage is proposed.

Abstract

With the exponential advancement of business technology in recent years, data-driven decision making has become the core of most industries. With the rise of new privacy regulations such as the General Data Protection Regulation in the European Union and the California Consumer Privacy Act in the United States, companies dealing with personal data had to conform to these changes and adapt their processes accordingly. This obviously included the transportation industry with their use of location data. At the other side of the spectrum, users still expect a form of personalization, without having to compromise on their privacy. For this reason, companies across the industries started applying privacy-enhancing or preserving technologies at scale in their products as a competitive advantage. In this paper, we describe how Federated Machine Learning can be applied to the transportation sector. We present use-cases for which Federated Learning is beneficial in transportation and the new product lifecycle that is required for using such a technology. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy and one that guides us beyond privacy-regulations and into the world of ethical data-usage.