From sensors to satellites, location data travels incredible distances each day.
For TomTom and other industry companies who want to stay ahead of the regulation curve, applying privacy-enhancing technologies (PET), is the next step towards a more secure and ethical usage of connected car data.
What are privacy-enhancing technologies?
Privacy-enhancing technologies are methods that allow for processing privacy-sensitive data securely with the goal of protecting the user’s personal information. They are an element of the privacy-by-design paradigm that intends to create systems and processes that at their core mitigate privacy risks without compromising the quality of the data-driven decisions.
Federated machine learning
One of the most promising PET solutions for the automotive industry is federated machine learning.
In traditional data analysis solutions, all user data is moved to a centralized remote server where the information can be analyzed by machine learning algorithms. But the rise of privacy regulations means companies now consider how to anonymize privacy-sensitive data and execute deletion at the customer's request. Without this data, it becomes difficult to perform advanced analytics with classical methods.
Federated machine learning is considered a privacy-aware machine learning method, where devices that collect data, perform computations locally and send only the aggregated results to a central entity. This adds a layer of protection to a user’s personal information while still allowing carmakers access to crucial information that can be used to improve products and services.
From a company perspective, federated learning ensures they can deliver the best tailored services, reduce cloud infrastructure costs and stay ahead in an ever-changing legal landscape.
