What are the main challenges in privacy-preserving NLP?

What are the main challenges in privacy-preserving NLP?

The main challenges include data anonymization, model robustness, and balancing privacy with performance.

Can you explain data anonymization in more detail?

Data anonymization involves removing personally identifiable information from datasets to protect user privacy.

How does model robustness relate to privacy?

Model robustness ensures that the NLP model performs well even when trained on anonymized data, which can be less informative.

What techniques are used to enhance privacy in NLP?

Techniques include differential privacy, federated learning, and secure multi-party computation.