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Many socially valuable activities depend on sensitive information, such asmedical research, public health policies, political coordination, andpersonalized digital services. This is often posed as an inherent privacytrade-off: we can benefit from data analysis or retain data privacy, but notboth. Across several disciplines, a vast amount of effort has been directedtoward overcoming this trade-off to enable productive uses of informationwithout also enabling undesired misuse, a goal we term `structuredtransparency'. In this paper, we provide an overview of the frontier ofresearch seeking to develop structured transparency. We offer a generaltheoretical framework and vocabulary, including characterizing the fundamentalcomponents -- input privacy, output privacy, input verification, outputverification, and flow governance -- and fundamental problems of copying,bundling, and recursive oversight. We argue that these barriers are lessfundamental than they often appear. Recent progress in developing`privacy-enhancing technologies' (PETs), such as secure computation andfederated learning, may substantially reduce lingering use-misuse trade-offs ina number of domains. We conclude with several illustrations of structuredtransparency -- in open research, energy management, and credit scoring systems-- and a discussion of the risks of misuse of these tools.

Beyond Privacy Trade-offs with Structured Transparency