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%. |
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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. |
Privacy-Preserving Natural Language Processing (2023.eacl-tutorials)
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| Challenge: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Approach: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Outcome: | The tutorial will cover membership inference, differential privacy, homomorphic encryption, or federated learning, all with typical use-cases and potential pitfalls. |
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. |
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TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)
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| Challenge: | Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers. |
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Privacy-preserving Neural Representations of Text (D18-1)
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| Challenge: | a specific type of attack is used to characterize the privacy of neural representations for NLP tasks, in the context of privacy protection. |
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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. |
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Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
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| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
| Approach: | They propose to use knowledge unlearning to reduce privacy risks for LMs by performing gradient ascent on target token sequences instead of trying to unlearn all the data at once. |
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CAPE: Context-Aware Private Embeddings for Private Language Learning (2021.emnlp-main)
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| Challenge: | Existing methods to obtain text representations or embeddings with these models encoding personally identifiable information may lead to privacy leaks. |
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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. |
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption (2022.naacl-main)
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| Challenge: | Embeddings compress information into low-dimensional vectors, but can leak private information about sensitive attributes of text. |
| Approach: | They propose a method to privatize embeddings based on homomorphic encryption to prevent leakage of sensitive information in the process of text classification. |
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