Security for Rural Public Computing
Bibliographic record
Abstract
Current research on securing public computing infrastructure like Internet kiosks has focused on the use of smartphones to establish trust in a computing platform or to offload the processing of sensitive information, and the use of new cryptosystems such as Hierarchical Identity-based Encryption (HIBE) to protect kiosk user data. Challenges posed by rural kiosks, specifically (a) the absence of specialized hardware features such as Trusted Platform Modules (TPMs) or a modifiable BIOS in older recycled PCs, (b) the potential use of periodically disconnected links between kiosks and the Internet, (c) the absence of a production-ready implementation of HIBE and (d) the limited availability of smartphones in most developing regions make these approaches difficult, if not impossible, to implement in a rural public computing scenario. In this thesis, I present a practical, unobtrusive and easy-to-use security architecture for rural public computing that uses a combination of physical and cryptographic mechanisms to protect user data, public computing infrastructure and handheld devices that access this infrastructure. Key contributions of this work include (a) a detailed threat analysis of such systems with a particular focus on rural Internet kiosks and handheld devices, (b) a security architecture for rural public computing infrastructure that does not require any specialized hardware, (c) an application-independent and backward-compatible security API for securely sending and receiving data between these systems and the Internet that can operate over delay tolerant links, \n(d) an implementation of my scheme for rural Internet kiosks and (e) a performance evaluation of this implementation to demonstrate its feasibility.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".