Evidence map›Paper›PMID 32131487›Full record

ArticleSensors (Basel, Switzerland)2020

SKINNY-Based RFID Lightweight Authentication Protocol.

Liang Xiao, He Xu, Feng Zhu, Ruchuan Wang, Peng Li

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. 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

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

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

5 authors.

Liang XiaoSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
He XuSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.ORCID 0000-0003-2809-2237
Feng ZhuSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.ORCID 0000-0002-7129-1082
Ruchuan WangSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Peng LiSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.ORCID 0000-0001-5026-5347

Funding

Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province No. 18KJA520008Scientific and Technological Support Project of Jiangsu Province No. BE2017166, and No. BE2019740the National Key R&D Program of China No. 2018YFB1003201the National Natural Science Foundation of P. R. China No. 61672296, No. 61602261, No. 61872196, No. 61872194 and No. 61902196
6 · The paper itself

Abstract

With the rapid development of the Internet of Things and the popularization of 5G communication technology, the security of resource-constrained IoT devices such as Radio Frequency Identification (RFID)-based applications have received extensive attention. In traditional RFID systems, the communication channel between the tag and the reader is vulnerable to various threats, including denial of service, spoofing, and desynchronization. Thus, the confidentiality and integrity of the transmitted data cannot be guaranteed. In order to solve these security problems, in this paper, we propose a new RFID authentication protocol based on a lightweight block cipher algorithm, SKINNY, (short for LRSAS). Security analysis shows that the LRSAS protocol guarantees mutual authentication and is resistant to various attacks, such as desynchronization attacks, replay attacks, and tracing attacks. Performance evaluations show that the proposed solution is suitable for low-cost tags while meeting security requirements. This protocol reaches a balance between security requirements and costs.

Indexed as

GNY logicmutual authenticationRFID systemsecurity protocolSKINNY

Identifiers

PMID32131487
PMCPMC7085710

What OpenQuestion holds

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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.