Only Pay What You Must Spend: On-Demand Privacy Budget Payment for Differentially Private RAG

arXiv:2609.27406 · cs.CR, cs.DL · Submitted 2026-09-23 · Read on arXiv

cs.CR, cs.DL

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: Accepted by EMNLP 2026 Main

Code: https://github.com/szzhh/SparsePay-RAG

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• RAG — 89× in this paper · explained in 3 episodes
• Clustering — 16× in this paper · explained in 2 episodes
• Differential Privacy — 14× in this paper · explained in 4 episodes
• Differentially Private — 10× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation — 8× in this paper
• AUC — 6× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Interrogation Attack — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Natural Questions — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Differential Privacy (DP) — 3× in this paper · explained in 7 episodes
• IEEE Symposium on Security and Privacy — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Dense Passage Retrieval — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Membership Inference Attack (MIA) — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Zero-Concentrated Differential Privacy — 1× in this paper

Sources

• Eliciting Latent Predictions from Transformers with the Tuned Lens
• Retrieval-Augmented Generation for Large Language Models: A Survey
• The Llama 3 Herd of Models
• Mistral 7B
• Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
• Harnessing large-language models to generate private synthetic text
• GPT-4 Technical Report
• OPT: Open Pre-trained Transformer Language Models

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