Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 266

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Unified Biometric Privacy Preserving Three-Factor Authentication and Key Agreement for Cloud-Assisted Autonomous Vehicles

Jiang, Qi, Zhang, Ning, Ni, Jianbing, Ma, Jianfeng, Ma, Xindi, Choo, Kim-Kwang Raymond·2020·IEEE Transactions on Vehicular Technology

Abstract

Autonomous vehicles (AVs) are increasingly common, although there remain a number of limitations that need to be addressed in order for their deployment to be more widespread. For example, to mitigate the failure of self-driving functions in AVs, introducing the remote control capability (which allows a human driver to operate the vehicle remotely in certain circumferences) is one of several countermeasures proposed. However, the remote control capability breaks the isolation of onboard driving systems and can be potentially exploited by malicious actors to take over control of the AVs; thus, risking the safety of the passengers and pedestrians (e.g., AVs are remotely taken over by terrorist groups to carry out coordinated attacks in places of mass gatherings). Therefore, security is a key, mandatory feature in the design of AVs. In this paper, we propose a cloud-centric three-factor authentication and key agreement protocol (CT-AKA) integrating passwords, biometrics and smart cards to ensure secure access to both cloud and AVs. Three typical biometric encryption approaches, including fuzzy vault, fuzzy commitment, and fuzzy extractor, are unified to achieve three-factor authentication without leaking the biometric privacy of users. Moreover, two session keys are negotiated in our protocol, namely: one between the user and AV to support secure remote control of the AV, and the other is negotiated between the mobile device and the cloud to introduce resilience to the compromise of ephemeral security parameters to ensure cloud data access security with a high security guarantee. Finally, we formally verify the security properties and evaluate the efficiency of CT-AKA, whose findings demonstrate that the protocol achieves high security strength with reasonable computation and communication costs.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tvt.2020.2971254

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 55 references

Cited by

See all 266

Metadata

Authors
Jiang, Q., Zhang, N., Ni, J., Ma, J., Ma, X., & Choo, K.-K. R.
Venue
IEEE Transactions on Vehicular Technology
Published
2020-09
Volume
69
Issue
9
Pages
9390-9401
ISSN
  • 0018-9545 (ISSN-L)
  • 1939-9359
OpenAlex
W3005206852
Cited by
266
References
55
FWCI
19.80
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Unified Biometric Privacy Preserving Three-Factor Authentication and Key Agreement for Cloud-Assisted Autonomous Vehicles. https://app.cochat.ai/papers/10.1109/tvt.2020.2971254
The paper
Jiang, Q., Zhang, N., Ni, J., Ma, J., Ma, X., & Choo, K.-K. R. (2020). Unified Biometric Privacy Preserving Three-Factor Authentication and Key Agreement for Cloud-Assisted Autonomous Vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2020.2971254

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.