Privacy-Aware Crowd Labelling for Machine Learning Tasks
This addresses privacy concerns for online users and researchers in academia, but it is incremental as it builds on existing text analysis and crowdsourcing methods.
The paper tackles the problem of privacy exposure in crowdsourced text labelling for machine learning tasks by proposing a privacy-preserving method that transforms text with varying privacy levels, showing that privacy can be implemented while retaining annotational diversity and subjectivity.
The extensive use of online social media has highlighted the importance of privacy in the digital space. As more scientists analyse the data created in these platforms, privacy concerns have extended to data usage within the academia. Although text analysis is a well documented topic in academic literature with a multitude of applications, ensuring privacy of user-generated content has been overlooked. Most sentiment analysis methods require emotion labels, which can be obtained through crowdsourcing, where non-expert individuals contribute to scientific tasks. The text itself has to be exposed to third parties in order to be labelled. In an effort to reduce the exposure of online users' information, we propose a privacy preserving text labelling method for varying applications, based in crowdsourcing. We transform text with different levels of privacy, and analyse the effectiveness of the transformation with regards to label correlation and consistency. Our results suggest that privacy can be implemented in labelling, retaining the annotational diversity and subjectivity of traditional labelling.