Abstract
User language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their data. In this work we propose SentDP, pure local differential privacy at the sentence level for a single user document. We propose a novel technique, DeepCandidate, that combines concepts from robust statistics and language modeling to produce high (768) dimensional, general 𝜖-SentDP document embeddings. This guarantees that any single sentence in a document can be substituted with any other sentence while keeping the embedding 𝜖-indistinguishable. Our experiments indicate that these private document embeddings are useful for downstream tasks like sentiment analysis and topic classification and even outperform baseline methods with weaker guarantees like word-level Metric DP.- Anthology ID:
- 2022.acl-long.238
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3367–3380
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.238
- DOI:
- 10.18653/v1/2022.acl-long.238
- Cite (ACL):
- Casey Meehan, Khalil Mrini, and Kamalika Chaudhuri. 2022. Sentence-level Privacy for Document Embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3367–3380, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Sentence-level Privacy for Document Embeddings (Meehan et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.acl-long.238.pdf
- Data
- IMDb Movie Reviews