Investigating Novice Researchers' Perceptions of Research Privacy Within LLM-Assisted Workflows

2026-06-02

AI Summary

This study investigates the privacy and intellectual property risks faced by early-career researchers when using large language models (LLMs) to support scientific work, along with their misperceptions regarding data leakage and associated coping strategies. Through semi-structured interviews with 44 interdisciplinary early-career researchers, combined with thematic coding, contextual analysis, and privacy threat modeling, the research uncovers a paradox wherein concerns about idea leakage inadvertently accelerate LLM adoption. It identifies five categories of user-devised privacy mitigation strategies that are widely perceived as ineffective. The findings reveal systematic misjudgments among users concerning data dilution effects and the adversarial value of shared information. Based on these insights, the study proposes practical interventions, including institutional sandboxing environments, context-aware privacy education, and verifiable data deletion mechanisms.

Abstract

Large Language Model (LLMs)-assisted scholarly workflows introduce critical privacy and intellectual property risks. As a uniquely vulnerable cohort driven by publication pressure and a lack of institutional support, novice researchers rely heavily on public LLMs, compelling them to navigate high-stakes privacy-publication trade-offs. To investigate these concerns, we conducted semi-structured interviews with 44 researchers across diverse disciplines. Our findings reveal that the fear of idea leakage paradoxically accelerates, rather than deters, reliance on LLMs, as researchers utilize them to expedite publication. They also held misconceptions that their ideas lacked the unique value to attract targeted attacks, and that their inputs would be safely diluted within massive datasets, preventing reconstruction. From interviews, we identified five types of mitigations including input fragmentation and adversarial probing, though we found that participants largely perceived these measures as ineffective. We outline implications including implementing institution-level sandboxed isolation, scenario-based privacy pedagogy, and verifiable data-deletion audits for transparency.

Problem

• research privacy
• novice researchers
• LLM-assisted workflows
• intellectual property risks
• privacy-publication trade-offs

Innovation

• LLM-assisted research
• research privacy
• novice researchers
• privacy misconceptions
• data deletion audit