The deployment of social robots in public spaces has received increased interest over the past years. These robots need to process a wide array of personal data to offer services that are tailored to users’ requirements. While much research has been carried out regarding the creation of explainable content, little research has dealt with how data transparency - as a way to address uncertainty and concerns regarding the handling of personal data - is conveyed to users. To examine the impact of different transparency declarations on trust, performance, and robot perception, we conducted a virtual reality (VR) supported laboratory experiment with N = 53 participants who interacted with a robot in a public setting (a library). The interaction between users and robots was accompanied by information on the handling of users’ personal data using three different modalities (via posters, the robot’s tablet, or verbally). The results imply that, while all modalities are understandable and perceived as useful, there is no preference for any modality. Our findings contribute to HRI research by examining different modalities for transparency declarations, in an effort to foster understandable and transparent processing of data.