A Multi-Dimensional Analysis of IoT Companion Apps: A Look at Privacy, Security and Accessibility
Bibliographic record
Abstract
Internet of Things (IoT) devices provide convenience to users by simplifying household tasks. Most IoTs can be remotely controlled via mobile companion apps, which constitute the main interface between devices themselves and their users. Such apps are used to configure, update, and control the device(s) and thus constitute a critical component in the IoT ecosystem. However, they have historically been understudied which prompts us to look into them. In this paper, we report on a study where we evaluated a sample of 455 IoT companion apps and analyze their privacy, security, and accessibility aspects. Our research aim is to understand these metrics, gauge their state and evaluate whether there is a correlation between them. Our primary findings from the analysis are: (i) most apps have reasonable security and accessibility posture, but in several dimensions there exists a long tail of apps with significant problems and (ii) apps tend to over-request permissions which are not related to their main goal. Moreover, the quality of an app along one aspect is uncorrelated to the same along other aspects. We conclude with actionable recommendations for companion app developers.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".