Evidence map›Paper›PMID 41894424›Full record

ArticlePloS one2026

Ensuring end-to-end privacy in IoT-based healthcare systems through hyperledger fabric and identity mixer.

Maryam Nasr Esfahani, Behrouz Shahgholi Ghahfarokhi, Shahram Etemadi Borujeni

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Maryam Nasr EsfahaniDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Behrouz Shahgholi GhahfarokhiFaculty of Computer Engineering, University of Isfahan, Isfahan, Iran.ORCID https://orcid.org/0000-0001-9768-8793
Shahram Etemadi BorujeniFaculty of Computer Engineering, University of Isfahan, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare information is often sensitive and confidential, and any unauthorized disclosure can jeopardize patient privacy. Centralized approaches currently used to protect patient information are vulnerable to a single point of failure and the risk of data breaches from central servers. Conversely, distributed blockchain-based methods tend to focus only on safeguarding data privacy for certain segments of the communication path from sensors to end users. This leaves a gap in methodologies that ensure both location and data privacy throughout the entire communication from sensors to healthcare data users via blockchain technology. In this paper, we propose using Hyperledger Fabric to ensure the immutability of patient data and to address the single point of failure issue. Additionally, to maintain end-to-end data and location privacy, our proposed scheme integrates the Idemix protocol suit alongside Hyperledger Fabric and employs a lightweight communication protocol for interactions between healthcare sensors and gateways. Compared to previous methods, our approach not only facilitates privacy features such as patient anonymity and untraceability but also guarantees data integrity, confidentiality, mutual authentication, and resilience against a variety of security threats, including collusion among internal entities. While the implementation of blockchain may introduce additional computational overhead for service providers compared to centralized systems, this trade-off is outweighed by the significant enhancements in security as evidenced by our analyses and evaluations.

Indexed as

Computer SecurityConfidentialityDelivery of Health CareInternet of ThingsBlockchainDigital HealthHumansPrivacy

Identifiers

PMID41894424
PMCPMC13028556

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.