Interview study of 44 novice researchers finds privacy fears paradoxically accelerate LLM use for faster publication, with misconceptions about idea value and data dilution, and perceived ineffective mitigations.
Privacy-preserving large language models: Mechanisms, applications, and future directions,
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HUOZIIME is an on-device LLM-powered input method with post-training on synthesized data and hierarchical memory that achieves efficient execution and memory-driven personalization.
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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Investigating Novice Researchers' Perceptions of Research Privacy Within LLM-Assisted Workflows
Interview study of 44 novice researchers finds privacy fears paradoxically accelerate LLM use for faster publication, with misconceptions about idea value and data dilution, and perceived ineffective mitigations.
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HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization
HUOZIIME is an on-device LLM-powered input method with post-training on synthesized data and hierarchical memory that achieves efficient execution and memory-driven personalization.
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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.