Evidence map›Paper›PMID 40615425›Full record

ArticleScientific reports2025

A lightweight and secure authentication and privacy protection scheme for internet of medical things.

Sanjay Kumar, Kumar Abhishek, Shitharth Selvarajan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Sanjay Kumar *Dept of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, 800005, India.
Kumar Abhishek *Dept of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, 800005, India.
Shitharth Selvarajan *CSD, Kebri Dehar University, 001, Kebri Dehar, Somali Regional, Ethiopia. ShitharthS@kdu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of smart healthcare technology has provided various benefits, including remote patient monitoring, personalized treatments, and early disease detection. However, transmitting sensitive patient data through IoMT devices raises significant security and privacy concerns. To protect patients' data and smart medical devices, authentication is required. We uncovered security flaws in the current healthcare architecture, including impersonation, stolen verifiers, and man-in-the-middle attacks. This encouraged us to propose a security architecture that protects patients' data from attacks and guarantees the security of sensitive healthcare information. Our proposed security scheme, ฿-ED-CRY (Bi Encryption Decryption Crypto), is based on Elliptic Curve Cryptography (ECC). It protects patients' sensitive healthcare information by enhancing privacy-preserving mechanisms and validates the effectiveness of our scheme. We implemented the proposed scheme using GCC 4.9.5 and the pairing-based cryptography (PBC) library. The results demonstrate that our scheme performs better than the existing security schemes in terms of privacy and computational efficiency. The computation cost is around 13.3640 percentage lower than other related schemes. This research explores the revolutionary effects of integrated healthcare systems, with an emphasis on the convergence of digital health technologies and patient treatment in real-world IoMT scenarios.

Indexed as

Computer SecurityConfidentialityInternet of ThingsAlgorithmsHumansPrivacyAuthenticationECCHealthcareInternet of medical thingsLightweightSchnorr

Identifiers

PMID40615425
PMCPMC12227687

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.