  In spite of the legal advances in personal data protection, the issue of
private data being misused by unauthorized entities is still of utmost
importance. To prevent this, Privacy by Design is often proposed as a solution
for data protection. In this paper, the effect of camera distortions is studied
using Deep Learning techniques commonly used to extract sensitive data. To do
so, we simulate out-of-focus images corresponding to a realistic conventional
camera with fixed focal length, aperture, and focus, as well as grayscale
images coming from a monochrome camera. We then prove, through an experimental
study, that we can build a privacy-aware camera that cannot extract personal
information such as license plate numbers. At the same time, we ensure that
useful non-sensitive data can still be extracted from distorted images. Code is
available at https://github.com/upciti/privacy-by-design-semseg .
