EncDBDB: Searchable Encrypted, Fast, Compressed, In-Memory Database\n using Enclaves
Bibliographic record
Abstract
Data confidentiality is an important requirement for clients when outsourcing\ndatabases to the cloud. Trusted execution environments, such as Intel SGX,\noffer an efficient, hardware-based solution to this cryptographic problem.\nExisting solutions are not optimized for column-oriented, in-memory databases\nand pose impractical memory requirements on the enclave. We present EncDBDB, a\nnovel approach for client-controlled encryption of a column-oriented, in-memory\ndatabases allowing range searches using an enclave. EncDBDB offers nine\nencrypted dictionaries, which provide different security, performance and\nstorage efficiency tradeoffs for the data. It is especially suited for complex,\nread-oriented, analytic queries, e.g., as present in data warehouses. The\ncomputational overhead compared to plaintext processing is within a millisecond\neven for databases with millions of entries and the leakage is limited.\nCompressed encrypted data requires less space than a corresponding plaintext\ncolumn. Furthermore, the resulting code - and data - in the enclave is very\nsmall reducing the potential for security-relevant implementation errors and\nside-channel leakages.\n
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".