CAPE: Context-Aware Private Embeddings for Private Language Learning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to obtain text representations or embeddings with these models encoding personally identifiable information may lead to privacy leaks. |
| Approach: | They propose a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings. |
| Outcome: | The proposed approach reduces private information leakage by 3% over the current method. |
Similar Papers
Privacy-preserving Neural Representations of Text (D18-1)
Copied to clipboard
| Challenge: | a specific type of attack is used to characterize the privacy of neural representations for NLP tasks, in the context of privacy protection. |
| Approach: | They propose several defense methods based on modified training objectives and characterize the tradeoff between privacy and the utility of neural representations. |
| Outcome: | The proposed defenses improve the privacy of neural representations and characterize the tradeoff between privacy and utility of representations. |
Learning and Evaluating a Differentially Private Pre-trained Language Model (2021.findings-emnlp)
Copied to clipboard
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias
| Challenge: | Contextual language models have improved performance but can lead to information leakage . |
| Approach: | They propose a differentially-private word-piece algorithm that allows training a tailored domain-specific vocabulary while maintaining privacy. |
| Outcome: | The proposed model can guarantee privacy while maintaining good model performance. |
TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers. |
| Approach: | They propose a framework for protecting inference privacy by applying random perturbations to clustered representations. |
| Outcome: | The proposed framework protects inference privacy by applying random perturbations to clustered representations. |
Differentially Private Language Models for Secure Data Sharing (2022.emnlp-main)
Copied to clipboard
| 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. |
TextHide: Tackling Data Privacy in Language Understanding Tasks (2020.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Federated Model Decomposition with Private Vocabulary for Text Classification (2022.emnlp-main)
Copied to clipboard
| 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. |
Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users. |
| Approach: | They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information. |
| Outcome: | The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility. |
Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed method can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being much more efficient and robust. |
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption (2022.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed method can protect embeddings from leakage while preserving their utility on downstream tasks. |