Authentication and securing personal information in an untrusted internet
Bibliographic record
Abstract
A large number of user PCs are currently infected with different types of malicious software including spyware, keyloggers, and rootkits. In general, any Internet-connected end-host cannot be fully trusted. In addition to this compromised host problem, attacks exploiting usability drawbacks of web services and security tools when used by everyday users, and semantic attacks such as phishing are commonly observed. In the given untrusted environment, traditional threat models which assume trusted end-hosts need to be re-evaluated. We propose a number of techniques to improve the trustworthiness of the web considering the current untrusted environment. To understand what is expected from regular users for performing sensitive online tasks, we review security requirements of six Canadian online banks, and identified an emerging gap between these requirements and usability. Instead of requiring users to follow an extensive list of security best-practices for online banking, we propose the Mobile Password Authentication (MP-Auth) protocol. Using a trusted personal device (e.g., cellphone) in conjunction with a PC, MP-Auth protects a user's long-term login credentials, and offers transaction integrity assuming the user PC is untrustworthy and the user is unaware of phishing attacks. MP-Auth's security largely depends on user-chosen passwords, which are generally weak. To assist users in generating strong but usable passwords, we propose an Object-based Password (ObPwd) scheme which creates text passwords from user-selected objects, e.g., photos or music files. As part of the compromised host problem, we further assume that sensitive identity numbers (e.g., Social Insurance Number) will eventually be breached. To reduce the value of compromised credential information to attackers in such a scenario, we propose the use of localized ID numbers that are valid only for a particular relying party. A similar localization approach for banking PINs to prevent exploitation of compromised PINs from intermediate banking switches is also proposed.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".