[Vision] Towards oblivious property graph databases
Bibliographic record
Abstract
Property graph databases are widely used in applications such as social networks and fraud detection due to their ability to model relationships between entities efficiently. However, outsourcing graph data to third-party cloud providers introduces significant privacy risks, as memory access patterns and result sizes can leak sensitive information even when data is encrypted. While prior work has developed oblivious relational databases to mitigate such attacks, no existing solution supports oblivious query processing for property graphs, which pose unique challenges beyond those addressed in relational settings. We present a vision for an oblivious property graph database that supports Cypher queries on encrypted outsourced data. We envision leveraging secure hardware enclaves such as Intel SGX/TDX and AMD SEV-SNP to protect query execution while novel query processing algorithms mitigate side-channel vulnerabilities through doubly oblivious query processing schemes. Specifically, we discuss the challenges in supporting oblivious query processing algorithms for multi-hop and cyclic queries without revealing data or memory access patterns. Our work highlights a crucial gap and potential research directions in oblivious query processing for property graph databases.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".