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To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?
March 26, 2024, 4:51 a.m. | Christopher Weiss, Frauke Kreuter, Ivan Habernal
cs.CL updates on arXiv.org arxiv.org
Abstract: 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. We argue that determining the $\varepsilon$ value should not be solely in the hands of researchers or system developers, but must also take into account the actual people who share their potentially sensitive data. …
abstract arxiv budget community cs.cl cs.cr data data sharing differential differential privacy framework interpretation key nlp nlp systems privacy risks systems the key training type
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