PropertyValue
?:abstract
  • 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.
is ?:annotates of
?:arxiv_id
  • 2012.08347
?:creator
?:externalLink
?:license
  • arxiv
?:pdf_json_files
  • document_parses/pdf_json/e526eedd2eabad316ca52da0ccea6eec5aa8ed9c.json
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • ArXiv
?:title
  • Beyond Privacy Trade-offs with Structured Transparency
?:type
?:year
  • 2020-12-15

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