Papers with favorite privacy frameworks
How reparametrization trick broke differentially-private text representation learning (2022.acl-short)
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| Challenge: | Differential privacy (DP) is a formal mathematical treatment of privacy protection . it guarantees how much privacy can be lost in the worst case . adapting DP mechanisms to NLP properly is largely non-trivial task . |
| Approach: | They propose to use differential privacy to learn text representations using DPText to quantify and guarantee how much privacy can be lost in the worst case. |
| Outcome: | The proposed methods are falsely claimed to be differentially private and violate privacy loss guarantees. |