New body-worn devices offer new, scalable user interfaces that are more intuitive and straightforward to use. However, body-hugging input and output poses serious new risks to user privacy: the large hand and finger gestures typically used for input are significantly more vulnerable to third-party observation than established forms of touch input. This applies even more to visual output on the body. This is particularly problematic since body-worn devices are typically used during mobile activities in non-private environments. The primary goal of this project is to contribute to the scalability of on-body computing in public environments by developing interaction techniques for the input and output of private information that provide improved resilience to privacy violations. At the heart of our approach is the goal of exploiting the unique interaction properties of the human body: high manual dexterity, high tactile sensitivity and a large available surface for input and output, coupled with the ability to flexibly shield input and output through variable posture. These properties can form the basis for new body-based input and output techniques that are scalable and (virtually) unobservable. This goal remains largely unexplored. It is very demanding due to the new and very different shapes and scales of body-hugging devices as well as the novel forms of multimodal input and output. These are further complicated by the inherent complexity of social environments, the proxemics involved, and the attention of users and bystanders. To create a design space for the interactions, we will empirically investigate the privacy of tactile input, visual and haptic output at different body locations, depending on posture and proxemic configurations. We will then systematically design and implement body-based input gestures and scalable techniques for multimodal interaction that preserve privacy in social environments under a generalized threat model. We use attention models that include the human body. The new interaction techniques are empirically evaluated with users in realistic scenarios and in the laboratory to assess how their characteristics affect usability, privacy and scalability. Both will help us understand the internal and external validity of our approach. We expect that the results of this project will significantly contribute to laying the foundations for scalable, privacy-preserving body-based interactions.