Risk-Aware Mobile App Security Testing: Safeguarding Sensitive User Inputs
Bibliographic record
Abstract
Over the years, mobile applications have brought about transformative changes in user interactions with digital services. Many of these apps however, are free and offer convenience at the cost of exchanging personal data. This convenience, however, comes with inherent risks to user privacy and security. This paper introduces a comprehensive methodology that evaluates the risks associated with sharing sensitive data through mobile applications. Building upon the Hierarchical Weighted Risk Scoring Model (HWRSM), this paper proposes an evaluation methodology for HWRSM, keeping in mind the implications of such risk scoring on real-world security scenarios. The methodology employs innovative risk scoring, considering various factors to assess potential security vulnerabilities related to sensitive terms. Practical assessments involving diverse set of Android applications, particularly in data-intensive categories, reveal insights into data privacy practices, vulnerabilities, and alignment with HWRSM scores. By offering insights into testing, validation, real-world findings, and model effectiveness, the paper aims to provide practical considerations to mobile application security discussions, facilitating informed approaches to address security and privacy concerns.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".