Glimmers: Resolving the Privacy/Trust Quagmire

By David Lie, Petros Maniatis. 2017

17 citations recorded by the source corpus.

Users today enjoy access to a wealth of services that rely on user-contributed data, such as recommendation services, prediction services, and services that help classify and interpret data. The quality of such services inescapably relies on trustworthy contributions from users. However, validating the trustworthiness of contributions may rely on privacy-sensitive contextual data about the user, such as a user's location or usage habits, creating a conflict between privacy and trust: users benefit from a higher-quality service that identifies and removes illegitimate user contributions, but, at the same time, they may be reluctant to let the service access their private information to achieve this high quality. We argue that this conflict can be resolved with a pragmatic Glimmer of Trust, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used on the client-side---in contrast to much recent work exploring SGX in cloud services---to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science, Privacy & Security, AI Ethics, Fairness & Society

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Secure aggregation can also be approached using trusted execution environments (introduced above), as in [269].”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “We have also argued that trusted hardware can validate the user’s data, to protect privacy and analysis integrity [47].”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, background): “We have also argued that trusted hardware can validate the user’s data, to protect privacy and analysis integrity [47].”