Lightweight Authentication Scheme for Healthcare With Robustness to Desynchronization Attacks
Bibliographic record
Abstract
Remote healthcare monitoring systems are gaining a lot of interest as they enable doctors to use public channels to get real-time data from the sensors placed in/on the patient’s body. This necessitates the implementation of a robust authentication scheme to ensure secure communication between trusted healthcare providers and sensors which are usually low in resources. To address these issues, in 2021, Masud et al. presented a lightweight anonymous user authentication scheme for securely obtaining patient’s real-time data. Their protocol is considered practical for deployment on sensor nodes as it only utilizes hash functions and does not require any public-key cryptography. In this work, we demonstrate how their protocol loses synchronization when a message is blocked/jammed and how in some scenarios, the protocol is exposed to the risk of session key disclosure and cannot ensure forward secrecy. To overcome these threats, we proposeLAPRD, a lightweight mutual authentication protocol that provides robustness to desynchronization attacks. The proposed scheme uses a one-way hash chain technique to ensure forward secrecy and enable resynchronization between the protocol entities in the event of a desynchronization attack.LAPRDalso achieves user and sensor node anonymity, thus ensuring privacy of the communicating entities. With the demonstration of both formal and informal analyses, the proposed protocol is ensured to withstand the identified attacks in Masud et al.’s scheme. The comparative analysis in terms of security and performance with relevant protocols indicates that the proposed protocol ensures higher security with considerably low computation and communication overheads, making it suitable for practical implementation in a lightweight healthcare environment.
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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 | 0.005 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".