Evidence map›Paper›PMID 35003323›Full record

ArticleComputational and mathematical methods in medicine2021

Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain.

Naif Almusallam, Abdulatif Alabdulatif, Fawaz Alarfaj

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Naif AlmusallamDepartment of Computer Science and Information, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0003-2400-616X
Abdulatif AlabdulatifDepartment of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.ORCID https://orcid.org/0000-0003-0646-5872
Fawaz AlarfajDepartment of Computer Science and Information, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-6598-6240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications.

Indexed as

Computer SecurityDelivery of Health CareInternet of ThingsPrivacyCloud ComputingComputational BiologyElectronic Health RecordsHumans

Identifiers

PMID35003323
PMCPMC8739545

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.