An Improved Conditional Privacy Protection Scheme Based on Ring Signcryption for VANETs
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) can effectively provide vehicle driving safety, high-speed data communication, intelligent traffic management, and vehicle entertainment. However, compared with traditional networks, VANETs are more vulnerable to attacks from adversaries, such as eavesdropping, tampering, tracking users’ privacy, etc. In order to provide security and privacy protection for VANET communication, many conditional privacy protection (CPP) authentication schemes have been reported. In 2021, Cai et al. designed a novel CPP scheme based on ring signcryption suitable for VANETs. They proved that their scheme enjoys confidentiality, unforgeability, anonymity of sender’s identity, and traceability of malicious vehicle users. But we demonstrate their scheme has some defects in construction and security. First, there is a small flaw in the ring signcryption algorithm in the scheme, which makes a legitimate receiver unable to get the original message sent by the sender from the received valid ciphertext. Second, Cai et al.’s scheme cannot provide anonymous protection of an honest sender’s identity. At the same time, it is unable to reveal the identity of a malicious vehicle user. Finally, we present an improved scheme of Cai et al.’s scheme and supply its security proofs in the random oracle model, and analyze its performance. Ours is superior to the original scheme in security and efficiency. It is very suitable for providing security and privacy protection for vehicular users in VANETs.
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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 | 0.001 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".