Privacy-Enhancing Data Filters

Visual or data modifications applied to training datasets that obscure the identity of contributors while preserving the information needed for machine learning tasks. In the context of sign language video, these filters may include face blurring, cel shading, avatar replacement, or silhouette rendering. Privacy-enhancing filters aim to address the tension between collecting sufficient training data from small, vulnerable populations and protecting contributors from identification, misuse, or discrimination. Research suggests that while filters may reduce individual data quality, they can increase overall participation enough to improve model performance through larger dataset sizes.