Towards Privacy-Enhancing Provenance Annotations for Images

The surge of media consumption on the Internet reflects how individuals engage with information, entertainment and communication. In parallel, the advancement of generative AI tools facilitates the creation of abundant content that is nearly indistinguishable from authentic content. To address aspects of misinformation, focus is shifted towards the secure and immutable annotation of provenance information. Although such frameworks aim to establish trust in media consumption, they raise privacy concerns, as provenance data may conceal identifiable information for individuals and locations. Thus, users should be allowed to manage the level of privacy of the provenance information of a media asset. In previous work, we focused on ensuring privacy in individual provenance events pertaining to the lifecycle of an image. This paper extends the privacy techniques over well-known provenance frameworks to allow a user to protect not only specific assertions of a provenance event but even subsets of the provenance graph.