Many socially valuable activities depend on sensitive information, such as medical research, public health policies, political coordination, and personalized digital services. This is often posed as an inherent privacy trade-off: we can benefit from data analysis or retain data privacy, but not both. Across several disciplines, a vast amount of effort has been directed toward overcoming this trade-off to enable productive uses of information without also enabling undesired misuse, a goal we term ‘structured transparency’. In this paper, we provide an overview of the frontier of research seeking to develop structured transparency. We offer a general theoretical framework and vocabulary, including characterizing the fundamental components – input privacy, output privacy, input verification, output verification, and flow governance – and fundamental problems of copying, bundling, and recursive oversight. We argue that these barriers are less fundamental than they often appear. Recent progress in developing ‘privacy-enhancing technologies’ (PETs), such as secure computation and federated learning, may substantially reduce lingering use-misuse trade-offs in a number of domains. We conclude with several illustrations of structured transparency – in open research, energy management, and credit scoring systems – and a discussion of the risks of misuse of these tools.