Research on blockchain-enabled consistency enhancement techniques for on-chain and off-chain interactions of privacy data
Bibliographic record
Abstract
In order to improve the consistency of on-chain-off-chain interaction of private data supported by blockchain and reduce the redundancy of data storage performance, this paper applies an efficient data interaction method of prefix hashing with improved red-black tree index to store public indexes and improve the efficiency of retrieval and interaction of blockchain data.Under the idea of generalization, anonymous region (AR) is used to hide the real location of participating nodes and protect the privacy of realized nodes.To reduce the computational overhead of the selection process, a cooperative sensing location privacy preserving optimization mechanism, LPPOM, is proposed.The scheme in this paper has a slow growth of data size on the chain with higher storage efficiency, larger throughput, and shorter query time (0.1899ms).The time cost consumed when the number of privacy chains is 15, 30, and 60 only increases by 0.2309-0.4855mscompared to the single chain system, indicating that the scheme scales well.When the file size is within 200 and the number of encrypted attributes is less than 4, its total encryption time meets the user's privacy data encryption needs (between 66.1765-236.7081ms).The IPFS read/write module is able to satisfy the people's daily use needs under the public network conditions, and its read/write speed is between 0.1568 and 0.2639MB/ms (file <100M).
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".