Cryptanalysis and Improvement of a Pairing-Free Certificateless Aggregate Signature in Healthcare Wireless Medical Sensor Networks
Bibliographic record
Abstract
The healthcare wireless medical sensor network is gradually changing the traditional mode of medical treatments with the rapid development of Internet of Things. Specifically, patients' healthcare data can be continuously collected by medical sensor nodes and transmitted to the medical specialists for disease monitoring, diagnosis and treatments. Recently, due to its advantages of low computational and communication overheads in a multiuser environment, the certificateless aggregate signature (CLAS) scheme has been adopted to prevent the sensitive healthcare data from being tampered and damaged, thereby ensuring the integrity and authenticity of data. In order to further improve the efficiency of CLAS schemes for the sensor nodes with limited resources, several CLAS schemes without bilinear pairing have been proposed. However, security issues prevent them from being fully applied in the practical scenarios. In this article, we analyze the security of a pairing-free CLAS scheme proposed by Liu et al. [IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5256-5266, 2020] by pointing out that their scheme is insecure against adversaries. After that, we introduce an improved scheme to solve the security vulnerability. The security proofs show that our improved scheme is existentially unforgeable against chosen message attacks under the random oracle model. In addition, the length of the aggregate signature in our proposal does not increase with the growth of the number of users, which greatly reduces the communication cost. Finally, the efficiency of our scheme is illustrated through both performance analyses and comparisons of related work.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".