  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.
