Papers with LDP
Privacy-Preserving News Recommendation Model Learning (2020.findings-emnlp)
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| Challenge: | Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. |
| Approach: | They propose a privacy-preserving method where user behavior data is locally stored on user devices to train accurate news recommendation models. |
| Outcome: | The proposed method can train accurate news recommendation models without centralized storage of user behavior data. |
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion (2022.naacl-main)
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| Challenge: | Existing methods to integrate text corpora with knowledge graphs (KGs) have been effective in various NLP tasks such as analyzing and predicting relationships between entities. |
| Approach: | They propose a method that borrows LDPs from entities that co-occur in sentences to represent entities that do not co-exist in a single sentence. |
| Outcome: | The proposed method improves the performance of prior methods such as TransE, DistMult, ComplEx and RotatE. |
Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer (2025.findings-emnlp)
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| Challenge: | RCP can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 PPL and 5.29 times reduced memory footprint. |
| Approach: | They propose a Quantization-Aware Training approach that uses extreme compression of LLMs . they propose 'rotate, clip, and partition' to learn non-uniform intervals and a GPU kernel supporting GEMV . |
| Outcome: | The proposed method can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 PPL and 5.29 times reduced memory footprint. |
DP-BART for Privatized Text Rewriting under Local Differential Privacy (2023.findings-acl)
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| Challenge: | Existing systems that guarantee privacy protection to individuals face several problems, such as formal mathematical flaws, unrealistic privacy guarantees, and privatization of only individual words. |
| Approach: | They propose a system that enables sharing of sensitive textual documents while formally guaranteeing privacy protection to individuals. |
| Outcome: | The proposed system outperforms existing LDP systems in terms of noise and privacy guarantees. |
Locally Differentially Private In-Context Learning (2024.lrec-main)
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| Challenge: | Large pretrained language models (LLMs) have shown surprising In-Context Learning ability. |
| Approach: | They propose a locally differentially private framework of in-context learning for LLMs that can be augmented with a private database for some specific task. |
| Outcome: | The proposed framework can predict labels without additional parameter modifications without input-label pairs . |
Agent-based Substructure Counting under Local Differential Privacy (2026.acl-long)
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| Challenge: | Recent studies have demonstrated the ability of Large Language Models (LLMs) to process graph problems. |
| Approach: | They propose to decompose substructure counting into node-level tasks distributed among node agents and embed the knowledge of distributed algorithms and DP frameworks in the curator agent and privacy controller. |
| Outcome: | Extensive experiments on 6 real-world datasets validate the effectiveness of the proposed framework for substructure counting tasks under edge local differential privacy (LDP). |
DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)
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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li
| Challenge: | Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage. |
| Approach: | They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation. |
| Outcome: | The proposed model excels on three datasets. |