Evidence map›Paper›PMID 32079301›Full record

ArticleSensors (Basel, Switzerland)2020

Lightweight Fine-Grained Access Control for Wireless Body Area Networks.

Mohammad Ali, Mohammad-Reza Sadeghi, Ximeng Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. 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.

Mohammad AliDepartment of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 159163-4311, Iran.
Mohammad-Reza SadeghiDepartment of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 159163-4311, Iran.
Ximeng LiuCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China.

Funding

National Natural Science Foundation of China 61702105National Natural Science Foundation of China U1804263Opening Project of Guangdong Provincial Key Laboratory of Data Security and Privacy Protection 2017B030301004-12
6 · The paper itself

Abstract

Wireless Body Area Network (WBAN) is a highly promising technology enabling health providers to remotely monitor vital parameters of patients via tiny wearable and implantable sensors. In a WBAN, medical data is collected by several tiny sensors and usually transmitted to a server-side (e.g., a cloud service provider) for long-term storage and online/offline processing. However, as the health data includes several sensitive information, providing confidentiality and fine-grained access control is necessary to preserve the privacy of patients. In this paper, we design an attribute-based encryption (ABE) scheme with lightweight encryption and decryption mechanisms. Our scheme enables tiny sensors to encrypt the collected data under an access control policy by performing very few computational operations. Also, the computational overhead on the users in the decryption phase is lightweight, and most of the operations are performed by the cloud server. In comparison with some excellent ABE schemes, our encryption mechanism is more than 100 times faster, and the communication overhead in our scheme decreases significantly. We provide the security definition for the new primitive and prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.

Indexed as

Computer SecurityRemote Sensing TechnologyWearable Electronic DevicesWireless TechnologyHumansPatientsPrivacyattribute-based encryptioncloud computingfine-grained access controllightweight computationwireless body area networks

Identifiers

PMID32079301
PMCPMC7071367

What OpenQuestion holds

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LicenceCC BY
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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.