# From Patient Burdens to User Agency: Designing for Real-Time Protection Support in Online Health Consultations

arXiv: https://arxiv.org/abs/2508.00328 · HTML: https://mlsanity.com/paper/2508.00328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models](https://mlsanity.com/paper/2508.07658/) (2025)

## Abstract (arXiv)

Online medical consultation platforms, while convenient, are undermined by significant privacy risks that erode user trust. We first conducted in-depth semi-structured interviews with 12 users to understand their perceptions of security and privacy landscapes on online medical consultation platforms, as well as their practices, challenges and expectation. Our analysis reveals a critical disconnect between users' desires for anonymity and control, and platform realities that offload the responsibility of ``privacy labor''. To bridge this gap, we present SafeShare, an interaction technique that leverages localized LLM to redact consultations in real-time. SafeShare balances utility and privacy through selectively anonymize private information. A technical evaluation of SafeShare's core PII detection module on 3 dataset demonstrates high efficacy, achieving 89.64% accuracy with Qwen3-4B on IMCS21 dataset.