arXiv subjects

Eve He

Publications and source records attributed to Eve He.

5 recordsLinked to original sources

arXiv2026

Generative AI-Enabled Refund Fraud in Chinese E-Commerce: Investigation on Merchants and Platform Workers

E-commerce dispute resolution typically relies on the security assumption that digital evidence truthfully reflects physical reality. Generative AI (GenAI) invalidates this threat model, enabling attackers to fabricate hyper-realistic evidence of product defects at negligible cost. Through semi-structured interviews with merchants (N=17) and platform workers (N=13) in the Chinese e-commerce market, we characterize this shift toward GenAI-enabled scalable fabrication. We outline a taxonomy of four GenAI-enabled threat vectors across the transaction, dispute, logistics and communication phases, highlighting how attackers exploit GenAI to synthesize physically plausible product defects at scale. To mitigate these threats, platforms and merchants are adapting verification strategies, relying on AI tools for automated screening and adversarial interrogation (e.g., requesting multi-angle videos) to increase attack complexity. However, we find several challenges that hinder the adoption of these defenses, including implementation hurdles like structural platform constraints and fundamental limitations regarding the technical sophistication of GenAI. We conclude by outlining design implications for privacy-preserving cross-platform fraud databases, and traceability mechanisms such as embedding verifiable material anchors into the product.

cs.CR↗

arXiv2026

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

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.

cs.HC↗

arXiv2026

A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI Perspective

Privacy Policies are a cornerstone of informed consent, yet a persistent gap exists between their legal intent and practical efficacy. Despite decades of Human-Computer Interaction (HCI) research proposing various visualizations, user comprehension remains low, and designs rarely see widespread adoption. To understand this landscape and chart a path forward, we synthesized 65 top-tier papers using a framework adapted from the user-centered design lifecycle. Our analysis presented findings of the field's evolution across four dimensions: (1) the trade-off between information load and decision efficacy, which demonstrates a shift from augmenting disclosures to prioritizing information condensation and cognitive load management to counter the inefficacy of comprehensive texts, (2) the co-evolutionary dynamic of design and automation, revealing that complex design ambitions such as context-awareness drove the need for advanced NLP, while recent LLM breakthroughs are enabling the semantic interpretation required to realize those designs, (3) the tension between generality and specificity, highlighting the divergence between standardized, cross-platform solutions and the increasing necessity for specialized, context-aware interaction patterns in IoT and immersive environments, and (4) balancing stakeholder opinions, which shows that visualization efficacy is constrained by the complex interplay of regulatory mandates, developer capabilities and provider incentives. We conclude by outlining four critical challenges for future research.

cs.HC↗

arXiv2025

Can Intelligent User Interfaces Engage in Philosophical Discussions? A Longitudinal Study of Philosophers' Evolving Perceptions

This study investigates the evolving attitudes of philosophy scholars towards the participation of generative AI based Intelligent User Interfaces (IUIs) in philosophical discourse. We conducted a three year (2023--2025) mixed methods longitudinal study with 16 philosophy scholars and students. Qualitative data from annual interviews reveal a three stage evolution in attitude: from initial resistance and unfamiliarity, to instrumental acceptance of the IUI as a tool, and finally to a deep principled questioning of the IUI's fundamental capacity for genuine philosophical thought. Quantitative data from blind assessments, where participants rated anonymized philosophical answers from both humans and an IUI, complement these findings. While participants acknowledged the IUI's proficiency in tasks requiring formal logic and knowledge reproduction, they consistently identified significant shortcomings in areas demanding dialectical reasoning, originality and embodied understanding. The study concludes that participants do not see the IUI as a peer but rather as a sophisticated mirror whose capabilities and limitations provoke a deeper reflection on the unique and irreplaceable human dimensions of philosophical inquiry, such as intuition, value laden commitment and the courage to question fundamental premises.

cs.HC↗

arXiv2025

Conflect: Designing Reflective Thinking-Based Contextual Privacy Policy for Mobile Applications

Privacy policies are lengthy and complex, leading to user neglect. While contextual privacy policies (CPPs) present information at the point of risk, they may lack engagement and disrupt tasks. We propose Conflect, an interactive CPP for mobile apps, guided by a reflective thinking framework. Through three workshops with experienced designers and researchers, we constructed the design space of reflective thinking-based CPP design, and identified the disconnect between context and action as the most critical problem. Based on participants' feedback, we designed Conflect to use sidebar alerts, allowing users to reflect on contextualized risks and fostering their control. Our system contextually detects privacy risks, extracts policy segments, and automatically generates risk descriptions with 94.0% policy extraction accuracy on CPP4APP dataset and a 4.35s latency. A user study (N=28) demonstrated that Conflect improves user understanding, trust, and satisfaction while lowering cognitive load compared to CPPs, privacy policies and privacy labels.

cs.HC↗