Supplementary Material AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy

Katharina Bendig, René Schuster, Nicole Thiemer, Karen Joisten, Didier Stricker

Abstract: In this supplementary material, we provide a more detailed overview of AnonyNoise, a method developed for predicting data-dependent noise aimed at preventing reidentification. The document is structured as follows: First, we detail the training parameters used in our implementation for the three datasets: DVS-Gesture [4], SEE [29], and Event-ReId [1], in order to ensure reproducibility. Next, we present numerical results from an inversion attack on our method, comparing its effectiveness to Gaussian noise when evaluated using a denoising network. This comparison provides insight into the robustness of AnonyNoise in contrast to traditional noise techniques in preventing data recovery and re-identification attempts. We moreover provide our statement regarding our responsibility to human subjects in the datasets used during our experiments. Lastly, we include an expanded set of visual examples across all datasets, including the results from image reconstruction attacks.

External IDs:dblp:conf/wacv/BendigSTJS25