Introduces DelegateCI-Bench (3167 samples) and a CI-guided RL query rewriter that improves privacy-utility tradeoff by up to +10.1 utility over on-device baselines.
Protecting user privacy in remote conversational systems: A privacy-preserving framework based on text sanitization
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 4representative citing papers
ConfusionPrompt enables private black-box LLM inference via prompt decomposition and pseudo-prompt mixing, claiming better privacy-utility trade-off than perturbation methods and lower memory use than open-source local models.
SharedRequest is a model-agnostic batch-level framework that mixes prompts with noise and groups equivalent instructions to achieve higher utility and lower query cost than individual differential privacy methods for LLM inference.
HERALD selectively encrypts sensitive tokens via medical NER, POS policies, and deterministic ciphertext substitution to enable privacy-preserving clinical LLM use while recovering near-plaintext task performance.
citing papers explorer
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Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
Introduces DelegateCI-Bench (3167 samples) and a CI-guided RL query rewriter that improves privacy-utility tradeoff by up to +10.1 utility over on-device baselines.
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ConfusionPrompt: Practical Private Inference for Online Large Language Models
ConfusionPrompt enables private black-box LLM inference via prompt decomposition and pseudo-prompt mixing, claiming better privacy-utility trade-off than perturbation methods and lower memory use than open-source local models.
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SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models
SharedRequest is a model-agnostic batch-level framework that mixes prompts with noise and groups equivalent instructions to achieve higher utility and lower query cost than individual differential privacy methods for LLM inference.
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Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models
HERALD selectively encrypts sensitive tokens via medical NER, POS policies, and deterministic ciphertext substitution to enable privacy-preserving clinical LLM use while recovering near-plaintext task performance.