A New Mutual Authentication and Key Agreement Protocol for Mobile Client—Server Environment
Bibliographic record
Abstract
Mobile devices are becoming an essential part of many users’ lives. Users exchange sometimes very sensitive data with remote servers. This raises a security problem in terms of the confidentiality and integrity of these data, and users’ privacy. Mutual authentication protocols allow a user and a server to confirm each other’s legitimacy and share a session key to encrypt subsequent communications. Several protocols have been proposed to achieve this goal. However, these have certain weaknesses, such as impersonation, lack of anonymity, the use of additional hardware, and the synchronization problem associated with the use of timestamps. In this paper, we propose a mutual authentication protocol based on elliptic curve cryptography for mobile client – server environments, which addresses the above problems. This protocol is intended to be lightweight as it is designed for resource constrained mobile devices. Moreover, we present a formal and informal analysis of the security of the proposed protocol. This latter has security attributes, such as session key security, perfect forward secrecy, user anonymity, resistance to impersonation, replay and insider attacks. Performance evaluation shows that we outperform similar protocols. Therefore, the proposed protocol is secure, efficient and suitable for mobile environments.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".