Smart City Gnosys

Smart city article details

Title On The Design Of Privacy-Aware Cameras: A Study On Deep Neural Networks
ID_Doc 39967
Authors Carvalho M.; Ennaffi O.; Chateau S.; Bachir S.A.
Year 2023
Published Lecture Notes in Computer Science, 13806 LNCS
DOI http://dx.doi.org/10.1007/978-3-031-25075-0_17
Abstract 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 on https://github.com/upciti/privacy-by-design-semseg. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords Deep learning; LPDR; Privacy by design; Privacy-aware camera; Semantic segmentation; Smart city


Similar Articles


Id Similarity Authors Title Published
42050 View0.871Dufraisse M.; Carvalho M.; Trouvé-Peloux P.; Champagnat F.Physics Based Camera Privacy: Lens And Network Co-Design To The RescueIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2024)
1618 View0.852Peng F.; Ping G.-L.; Ge S.-K.A Face Privacy Protection Scheme Using Cnn Based Roi EditingProceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019)