Meaningful Anonymization for Privacy-Preserving Video Generation

Videos contain rich visual and temporal information for understanding human activities, but can also expose sensitive information about people and their surroundings. Existing privacy-preserving video methods primarily reduce such leakage by suppressing or discarding privacy-sensitive visual information, which can inadvertently remove content useful for understanding the video. We introduce **Mask2Safe**, a privacy-preserving video generation framework that learns *where* privacy-preserving modifications are needed and uses this localization to guide *how* sensitive content is transformed. Rather than relying on the costly vision-language reasoning for video frames, Mask2Safe learns semantic privacy localization from paired unsafe–safe images through a lightweight *Privacy Masker*. Its predictions are refined and propagated over the whole clip to produce temporally consistent masks that support either mask-based or generative anonymization. We further introduce **SafeQA**, a video question-answering benchmark that evaluates meaningful anonymization along three complementary dimensions: *Privacy*, whether the original sensitive information remains recoverable; *Visibility*, whether the affected visual content remains observable; and *Local Utility*, whether nearby non-sensitive information remains recoverable. Experiments on VP-UCF101 and VP-HMDB51 show that Mask2Safe reduces the predictability of sensitive attributes while retaining downstream utility on action recognition task, and that classifiers trained on our anonymized videos are able to transfer effectively to the original video domain. SafeQA further shows that generative anonymization substantially improves visibility over mask-based anonymization while maintaining strong local utility.