MultiFile View: File-View-Based Isolation in a Single-User Environment to Protect User Data Files
Bibliographic record
Abstract
Isolation technology is often used to reduce the impact of cyber attacks, and it is mainly used in multi-user environments. Representative examples of said technology include access-control mechanisms and virtual machines. In a single-user environment, virtual addressing and a trusted execution environment isolate applications. However, the focus of such techniques is usually only on isolation, while the sharing of files has not been given much attention. In a single-user environment, users have the ability to access the same file through multiple applications. In this paper, we introduce the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">file view</i>, and propose file isolation based on the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">views</i>. Under the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiFile View</i> mechanism, files within a view can be accessed by multiple applications when that particular view is activated. In other words, files appear only if their associated view is activated. The proposed technique is effective in protecting files from attacks on user data files, such as ransomware, wiper, and evil maid attacks. We also develop three models to describe how to assign views to applications. The proposed technique is prototyped in Windows 10. Through extensive experiments, we demonstrate that the new technique’s performance overhead does not noticeably affect the overall user experience.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".