IntactEdge: Secure Multi-Replica Data Integrity Verification in Mobile Edge Computing
Bibliographic record
Abstract
In this paper, we propose a new secure data integrity verification scheme for mobile edge computing (MEC), named IntactEdge, which provides an efficient method for end users in MEC to verify that the edge servers correctly maintain their stored data. Particularly, IntactEdge is designed using lightweight cryptographic mechanisms, such as message authentication codes and hash functions, to ensure efficiency in generating and verifying data integrity proofs. Batch verification is also supported to further enhance computational efficiency for end users during integrity checks. In addition, IntactEdge offers the desirable feature of multi-replica secure storage, allowing user data to be distributed across multiple edge servers to enhance data availability. To prevent edge nodes from exploiting data replicas stored on other nodes to falsely satisfy integrity verification requests, IntactEdge generates distinct data replicas for different edge nodes, while still enabling each replica to support proof generation using the same authentication tag. Finally, we demonstrate that IntactEdge is secure against potential data integrity attacks on edge servers and highly efficient in both tag generation and integrity verification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".