Security, Privacy, and Trust for Pervasive Computing Applications
Bibliographic record
Abstract
Pervasive computing has progressed significantly during this decade due to the developments and advances in portable, low-cost, and light-weight devices along with the emergence of short range and low-power wireless communication networks. Pervasive computing focuses on combining computing and communications with the surrounding physical environment to make computing and communication transparent to the users in day-to-day activities. In pervasive computing, numerous, casually accessible, often invisible, frequently mobile or embedded devices form an ad-hoc network that occasionally connects to fixed networks structure too. These pervasive computing devices often collect information about the surrounding environment using various sensors. Pervasive computing has the inherent disadvantages of slow, expensive connections, frequent line disconnections, limited host bandwidth, location dependent data, and so forth. These challenges make pervasive computing applications more vulnerable to various security-related threats. However, traditional security measures do not fit well in pervasive computing applications. Since location and context are key attributes of pervasive computing applications, privacy issues need to be handled in a sophisticated manner. The devices in a pervasive computing network leave and join in an ad-hoc manner. This device behavior creates a need for new trust models for pervasive computing applications. In this chapter, we address the challenges and requirements of security, privacy, and trust for pervasive applications. We also discuss the state-of-the-art of pervasive security, privacy, and trust along with some open issues.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".