To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?

no code implementations • 13 Jul 2023 • Christopher Weiss, Frauke Kreuter, Ivan Habernal

Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the key parameter, privacy budget $\varepsilon$ that governs the strength of privacy protection, remains largely arbitrary.

Decision Making Privacy Preserving