Shroud: ensuring private access to large-scale data in the data center
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".