PIM-ORAM: Towards Oblivious RAM Primitives in Commodity Processing-In-Memory
Bibliographic record
Abstract
Oblivious RAM (ORAM) is theoretically proven to render memory access patterns of a computation completely uniform, mitigating memory side-channel attacks. However, it is accompanied by orders of magnitude slower memory access latency and, thus, is often impractical in many circumstances. On the other hand, Processing-In-Memory (PIM) has been advancing as a solution to accelerate memory-intensive work-loads and mitigate the memory wall problem. In this paper, we explore the new direction of in-DRAM oblivious RAM with a design named PIM-ORAM. We retrofit the currently available commodity PIM hardware to provide future direction for secure computation on PIM, and design PIM-ORAM. Our design proposes split-data ORAM, a parallelizable in-memory ORAM scheme that takes full advantage of the parallel computing power of the PIM while retaining the original security guarantee of ORAM and dealing with the constraints existing in the commodity PIM. We evaluate PIM-ORAM using the PIM -enabled testbed cloud to provide more realistic numerical values. The evaluation shows that PIM-ORAM alleviates the increase of memory bus usage and ORAM access latency when the ORAM capacity increases.
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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 | 0.002 |
| 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".