Harnessing Language Models to Analyze Android App Permission Fidelity
Bibliographic record
Abstract
Android’s vast app ecosystem (over 2 million apps) poses significant privacy risks, as current methods for inferring permissions from descriptions - keyword matching, traditional natural language processing (NLP), and recurrent neural networks (RNNs) - struggle with accurate inference due to imprecise, ambiguous, or incomplete natural language descriptions. This gap undermines regulatory transparency and user trust, necessitating tools that reconcile stated functionality with actual data practices. We demonstrate that large language models like GPT-4o, applied in a zero-shot inference setting, leverage contextual reasoning to infer permissions competitively, while fine-tuned encoders (BERT, BART) surpass state-of-the-art performance when trained on minimally annotated datasets augmented with paraphrases, achieving $50-70 \%$ gains in weighted and macro $F_{1}$ scores. By enabling precise permission auditing with reduced annotation costs, our work advances scalable, adaptable solutions for privacy compliance across resource-constrained and highstakes environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".