Evidence map›Paper›PMID 42092012›Full record

ArticleScientific reports2026

Identity trust management based on differential privacy for internet of vehicles.

Liang Ren, Hui Li, Yutong Liu, Dan Liao, Ming Zhang, Haiyan Jin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Liang RenUniversity of Electronic Science and Technology of China, Chengdu, China.
Hui LiUniversity of Electronic Science and Technology of China, Chengdu, China.
Yutong LiuUniversity of Electronic Science and Technology of China, Chengdu, China.
Dan LiaoUniversity of Electronic Science and Technology of China, Chengdu, China. dliao.uestc@gmail.com.
Ming ZhangUniversity of Electronic Science and Technology of China, Chengdu, China.
Haiyan JinUniversity of Electronic Science and Technology of China, Chengdu, China.

Funding

Ji'an City Natural Science Foundation 20244-018635Ji'an City Science and Technology Special Project 2024-01-0103Ji'an City Science and Technology Special Project 2024-01-0201Natural Science Foundation of Jiangxi Province 20242BAB25071Sichuan Province Science and Technology Project 2025YFHZ0026
6 · The paper itself

Abstract

Vehicle trust management is closely related to the identity security of intra-domain vehicles and has garnered widespread attention. However, there is a significant conflict between vehicle trust and identity privacy, which has led to the emergence of novel trust link attack issues. To address the problem, this paper proposes a vehicle identity trust management algorithm based on differential privacy, called DITDP. In DITDP, we first design an identity trust evaluation model based on D-S evidence, which is used to quantify vehicle trust values, including the direct trust, the recommended trust, and the aggregated trust. Then, we design the dynamic trust evaluation and identity privacy protection modules in DITDP. The dynamic trust evaluation module corrects conflict between vehicle trust components and can dynamically update vehicle trust values in the time domain. The identity privacy protection module achieves indistinguishable trust while ensuring the availability of trust value by using Differential privacy. Finally, the simulations verify that the DITDP algorithm performs well in terms of identity protection ability, trust availability, and other aspects.

Indexed as

Differential privacyIdentity securityIntra-domain vehicleTrust management

Identifiers

PMID42092012
PMCPMC13338391

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.