A look into user’s privacy perceptions and data practices of IoT devices
Bibliographic record
Abstract
Purpose With the rapid deployment of internet of things (IoT) technologies, it has been essential to address the security and privacy issues through maintaining transparency in data practices. The prior research focused on identifying people's privacy preferences in different contexts of IoT usage and their mental models of security threats. However, there is a dearth in existing literature to understand the mismatch between user's perceptions and the actual data practices of IoT devices. Such mismatches could lead users unknowingly sharing their private information, exposing themselves to unanticipated privacy risks. The paper aims to identify these mismatched privacy perceptions in this work. Design/methodology/approach The authors conducted a lab study with 42 participants, where they compared participants’ perceptions with the data practices stated in the privacy policy of 28 IoT devices from different categories, including health and exercise, entertainment, smart homes, toys and games and pets. Findings The authors identified the mismatched privacy perceptions of users in terms of data collection, sharing, protection and storage period. The findings revealed the mismatches between user's perceptions and the data practices of IoT devices for various types of information, including personal, contact, financial, heath, location, media, connected device, online social media and IoT device usage. Originality/value The findings from this study lead to the recommendations on designing simplified privacy notice by highlighting the unexpected data practices, which in turn, would contribute to the secure and privacy-preserving use of IoT devices.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".