Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
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It has been demonstrated that hidden representation learned by deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy.To address this issue, we propose a novel approach called Differentially Private Neural Representation (DPNR) to preserve privacy of the extracted representation from text.DPNR utilises Differential Privacy (DP) to provide formal privacy guarantee.Further, we show that masking words via dropout can further enhance privacy.To maintain utility of the learned representation, we integrate DP-noisy representation into a robust training process to derive a robust target model, which also helps for model fairness over various demographic variables.Experimental results on benchmark datasets under various parameter settings demonstrate that DPNR largely reduces privacy leakage without significantly sacrificing the main task performance.
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Xu et al. demonstrated that MultiKrum is not robust against untargeted poisoning.
“Xu et al. [148] demonstrated that MultiKrum is not robust against the untargeted poisoning.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [148]
Recent inspections revealed that previous Byzantine-robust FL mechanisms are brittle and can be easily circumvented.
“In spite of their robustness guarantees, recent inspections revealed that previous Byzantine-robust FL mechanisms are also quite brittle and can be easily circumvented.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [148]
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Ye, Z., Dai, M., Guan, Z., Ma, C., & Huang, H. (2026) · Journal of King Saud University: Computer and Information Sciences · doi:10.1007/s44443-026-00512-0
Liu, Y., Han, W., Cai, C., Yuan, X., & Wang, C. (2026) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom59046.2026.11571688
RadSLDP: Selective Local Differential Privacy for Radiology Vision-Language Models
Zhao, K., Xu, X., Xu, R., Natsuaki, Y., Xiong, W., Karimireddy, S. P., & Liu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_46
- Authors
- Lyu, L., He, X., & Li, Y.
- Affiliations
- National University of Singapore (SG)
- Monash University (AU)
- The University of Melbourne (AU)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 2355-2365
- OpenAlex
- W3098049952
- Cited by
- 51
- References
- 46
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 9 · 2023: 13 · 2022: 12 · 2021: 8
- FWCI
- 3.15
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- differential privacy, neural representation learning, privacy leakage, robust training, model fairness, demographic fairness, natural language processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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