Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Nina Mouhammad, Johannes Daxenberger, Benjamin Robert Schiller, Ivan Habernal · 2023
Most tasks in NLP require labeled data.Data labeling is often done on crowdsourcing platforms due to scalability reasons.However, publishing data on public platforms can only be done if no privacy-relevant information is included.Textual data often contains sensitive information like person names or locations.In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing.We find that DPrewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data.PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.