Shroud: ensuring private access to large-scale data in the data center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent events have shown online service providers the perils of possessing private information about users. Encrypting data mitigates but does not eliminate this threat: the pattern of data accesses still reveals information. Thus, we present Shroud, a general storage system that hides data access patterns from the servers running it, protecting user privacy. Shroud functions as a virtual disk with a new privacy guarantee: the user can look up a block without revealing the block's address. Such a virtual disk can be used for many purposes, including map lookup, microblog search, and social networking.Shroud aggressively targets hiding accesses among hundreds of terabytes of data. We achieve our goals by adapting oblivious RAM algorithms to enable large-scale parallelization. Specifically, we show, via new techniques such as oblivious aggregation, how to securely use many inexpensive secure coprocessors acting in parallel to improve request latency. Our evaluation combines large-scale emulation with an implementation on secure coprocessors and suggests that these adaptations bring private data access closer to practicality.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.008 |
| 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 it