Papers with TextHide
Differentially Private Instance Encoding against Privacy Attacks (2022.naacl-srw)
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| Challenge: | TextHide is a proposed privacy-enhancing technology to protect the training data from privacy attacks. |
| Approach: | They propose to encode training data via instance encoding in natural language domain without theoretic privacy guarantee. |
| Outcome: | The proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off). |
TextHide: Tackling Data Privacy in Language Understanding Tasks (2020.findings-emnlp)
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| Challenge: | Unsolved privacy challenges in distributed or federated learning are a challenge for many domains including Natural Language Processing. |
| Approach: | They propose a federated learning framework that adds an encryption step to prevent an eavesdropping attacker from recovering private text data. |
| Outcome: | The proposed model can effectively defend against attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%. |
Reconstruction Attack on Instance Encoding for Language Understanding (2021.emnlp-main)
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| Challenge: | Existing private learning schemes which protect data privacy can be used to train models using instance encoding. |
| Approach: | They propose to recover the private training data and use it to break a private learning scheme TextHide. |
| Outcome: | The proposed attack would advance privacy-preserving machine learning in the context of natural language processing. |