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.

本文概述了当今为实现 “结构透明” 而进行的研究的前沿，提供了一个概括性的理论框架和词汇，包括表征基本组件 - 输入隐私、输出隐私、输入验证、输出验证和流程治理 - 以及复制、捆绑和递归监督等基本问题，并论证了最近随着 “隐私增强技术” 的发展，如安全计算和联邦学习，可以在许多领域显着减少潜在的使用 - 滥用的权衡。本文最后给出了结构透明的几个应用示例，包括开放式研究、能源管理和信用评分系统，并讨论了这些工具被滥用的风险。