python-pillow/Pillow 8.0.0

Kemenade, Hugo van; wiredfool,; Murray, Andrew; Clark, Alex M.; Karpinsky, Alexander; nulano,; Gohlke, Christoph; Dufresne, Jon; Crowell, Brian; Schmidt, David A.; Houghton, Alastair; Kopachev, Konstantin; Mani, Sandro; Landey, Steve; vashek,; Ware, Josh; Jason,; Caro, David; Kossouho, Steve; Lahd, Riley; Stanislau, T; Lee, Antony; Brown, Eric W.; Tonnhofer, Oliver; Bonfill, Mickael; Rowlands, Peter; Al-Saidi, Fahad; Górny, Michał; Korobov, Mikhail; Kurczewski, Marcin

doi:10.5281/zenodo.4088798

Cited by 1 publication

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“…In addition, the images or videos can contain text which may include usernames, person names or other sensitive information. We detect faces in visual content using multi-task Cascaded Convolutional Networks [36] in Facenet Pytorch [11] and blur all occurrences using the Python Imaging Library [34]. We detect text using a pre-trained [35] EAST text detection model [37] and blur all occurrences using the Gaussian blur option provided by OpenCV [7].…”

Section: De-identifying Pii In Mediamentioning

confidence: 99%

Automatic de-identification of data download packages

Boeschoten

Voorvaart

Goorbergh

et al. 2021

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The General Data Protection Regulation (GDPR) grants all natural persons the right to access their personal data if this is being processed by data controllers. The data controllers are obliged to share the data in an electronic format and often provide the data in a so called Data Download Package (DDP). These DDPs contain all data collected by public and private entities during the course of a citizens’ digital life and form a treasure trove for social scientists. However, the data can be deeply private. To protect the privacy of research participants while using their DDPs for scientific research, we developed a de-identification algorithm that is able to handle typical characteristics of DDPs. These include regularly changing file structures, visual and textual content, differing file formats, differing file structures and private information like usernames. We investigate the performance of the algorithm and illustrate how the algorithm can be tailored towards specific DDP structures.

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