Developing a machine learning-based vehicular safety system that is effective and generalizes well, capable of coping with all the different scenarios in real traffic is a challenge that requires large amounts of data. Especially visual data for when you want an autonomous vehicle to make decisions based on peoples’ possible intent revealed by the facial expression and eye gaze of nearby pedestrians. The problem with collecting this kind of data is the privacy issues and conflict with current laws like General Data Protection Regulation (GDPR). To deal with this problem we can anonymise faces with current identity and face swapping techniques. To evaluate the performance and interpretation of the anonymization process, there is a need for a metric to measure how well these faces are anonymized that takes identity leakage into consideration. To our knowledge, there is currently no such investigation for this problem. However, our method is based on current facial recognition methods and how recent face swapping work determines identity transfer performance. Our suggestion is to utilize state-of-the-art identity encoders like FaceNet and ArcFace to make use of the embedding vectors to measure anonymity. We provide qualitative results that show the applicability of publicly available identity encoders for measuring anonymity. We further strengthen the applicability of how these encoders behave on the VGGFace2 dataset compared to samples that have had their identity changed by Faceshifter, along with a survey regarding the anonymization procedure to pinpoint how strong facial anonymization is compared the vector distance measurements.