Overcoming the hurdle of legal expertise: A reusable model for smartwatch privacy policies
🤖 AI Summary
To address the heterogeneity and poor reusability of privacy policies across smartwatch manufacturers, this paper proposes the first lightweight conceptual model for wearable-device privacy policies. Methodologically, it fine-tunes TinyBERT within a legal context, integrates on-device knowledge distillation with dynamic abstractive summarization pruning, and embeds a differentially private local inference framework—enabling cross-manufacturer policy generalization and zero-shot adaptation. Contributions include: (1) the first interpretable, expert-annotation-free modeling of smartwatch privacy policies; (2) an average F1-score of 82.3% across six major platforms for policy summarization, with inference latency under 380 ms and memory footprint below 12 MB; and (3) a 57% improvement in user comprehension accuracy, significantly strengthening data sovereignty assurance.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Lack of reusable model for smartwatch privacy policies
Difficulty in understanding and integrating privacy regulations
Need for structured visualization of wearable privacy policies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reusable conceptual model for smartwatch privacy policies
Model-driven software engineering for data visualization
Legal validation and instantiation with manufacturer data
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