| Challenge: | Encoded text representations often capture sensitive attributes about individuals, raising privacy concerns and making models unfair to certain groups. |
| Approach: | They propose an approach that combines privacy and adversarial training to learn private representations which induces fairer models. |
| Outcome: | The proposed approach improves on four NLP datasets and shows that privacy and fairness can positively reinforce each other. |
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Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness (2020.findings-emnlp)
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| Challenge: | Existing approaches to learn text representations can encode private information of the input, thus can be exploited to recover such information with reasonable accuracy. |
| Approach: | They propose a novel approach to preserve privacy of the extracted representation from text by combining differential privacy with dropout. |
| Outcome: | The proposed approach preserves privacy of the extracted representation from text while masking words via dropout can enhance privacy. |
Federated Model Decomposition with Private Vocabulary for Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to train federated learning (FL) for natural language processing require sensitive data to leave local devices. |
| Approach: | They propose a fedrated model decomposition method that protects the privacy of vocabularies . they propose an adaptive updating technique to improve the performance of local models . |
| Outcome: | The proposed method protects the privacy of vocabularies in federated learning tasks . it maintains competitive performance and provides better privacy-preserving capacity compared to status quo methods. |
Trade-Offs Between Fairness and Privacy in Language Modeling (2023.findings-acl)
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| Challenge: | Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. |
| Approach: | They propose to incorporate privacy preservation and de-biasing techniques into training text generation models to investigate the trade-off between the two dimensions. |
| Outcome: | The proposed model improves on bias detection, privacy attacks, language modeling, and performance on downstream tasks. |
Differentially Private Language Models for Secure Data Sharing (2022.emnlp-main)
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| Challenge: | a variety of deanonymization attacks allow the re-identification of individuals from tabular data. |
| Approach: | They propose to train a language model in a differentially private manner and sample data from it . they find that the model generates fluent textual datasets with privacy guarantees . |
| Outcome: | The proposed methods outperform direct classifiers with DP-SGD in the real-world. |
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. |
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation (2026.acl-long)
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| Challenge: | Existing LLMs require users to submit raw text regardless of its sensitivity, resulting in substantial computational overhead and degrade model performance. |
| Approach: | They propose a new training pipeline that allows a client-side encoder to condition on k-pooled prompt embeddings instead of raw text and a server-side projection module to fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddables. |
| Outcome: | The proposed approach eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers. |
Empirical Studies of Institutional Federated Learning For Natural Language Processing (2020.findings-emnlp)
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| Challenge: | federated learning is a promising ideology to unite isolated datasets for machine learning problems. |
| Approach: | They propose to use federated natural language processing networks to train a popular NLP model with applications in sentence intent classification. |
| Outcome: | The proposed model is sensitive to imbalanced data load and tested against a federated model under imbalanced datasets. |
One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks (2022.emnlp-main)
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| Challenge: | Existing research on the efficiency of differentially-private stochastic gradient descent (DP-SGD) in NLP is inconclusive or even counter-intuitive. |
| Approach: | They propose to use differentially-private stochastic gradient descent (DP-SGD) to preserve privacy in NLP by using modern neural models based on BERT and XtremeDistil architectures to conduct extensive experiments. |
| Outcome: | The proposed models and training strategies provide the best trade-off between privacy and performance on different NLP tasks. |
ADePT: Auto-encoder based Differentially Private Text Transformation (2021.eacl-main)
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| Challenge: | Differential privacy is an important privacy concern when building statistical models on data containing sensitive information. |
| Approach: | They propose a utility-preserving differentially private text transformation algorithm using auto-encoders that can be used to transform text to offer robustness against attacks and produce transformations with high semantic quality. |
| Outcome: | The proposed model performs better against membership inference attacks while offering lower to no degradation in the utility of the underlying transformation process compared to baselines. |
TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)
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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies . |
| Approach: | They propose a method to preserve inference privacy by fusing token representations in the cloud. |
| Outcome: | The proposed method preserves inference privacy without sacrificing performance on different scenarios. |