A Study on the Privacy Concerns of the Internet of Things
Bibliographic record
Abstract
The widespread adoption of mobile devices, including smartphones, wearables and the Internet of Things (IoT), fostered the potential for data collection and online sharing in large scale. While users tend to be enthusiastic about the opportunities to share information about their everyday activities, there is a growing concern on privacy. To devise solutions for privacy-enhancing technologies that meet users' needs, we need to better understand their current concerns and practices. In this work, we applied an online survey to over 1,000 participants. We analyze their responses and in this paper we discuss the end users' perspective on IoT privacy, further eliciting the gaps of the Privacy Paradox. The results show that although users are concerned about the data privacy breach, certain privacy breaches tend to be more often considered by users. The technology experience of participants has a strongly or moderately significant relationship with most of their answers. The results indicate that there is a considerable percentage of users (24%) need assistance in making privacy-related decisions. Novel solutions for privacy-enhancing technologies need yet to be developed to ensure that users understand, consider and control their private information.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".