Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
This addresses privacy concerns for NLP researchers and practitioners using crowdsourcing platforms, but it is incremental as it builds on existing PII removal and DP techniques.
The paper tackled the problem of enabling crowdsourcing on sensitive textual data by investigating privacy-preserving methods, finding that differential privacy rewriting preserves privacy with good label quality for certain tasks, while PII removal yields good label quality across tasks but lacks privacy guarantees.
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 DP-rewriting 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.