Evidence map›Paper›PMID 35400843›Full record

ArticleComputer standards & interfaces2023

Privacy-preserving COVID-19 contact tracing solution based on blockchain.

Momeng Liu, Zeyu Zhang, Wenqiang Chai, Baocang Wang

Open access · greenAbstract read
In one paragraph

Article in Computer standards & interfaces, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

5 citing papers in PubMed, 32 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Momeng LiuShaanxi Key Laboratory of Clothing Intelligence, State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.
Zeyu ZhangShaanxi Key Laboratory of Clothing Intelligence, State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.
Wenqiang ChaiShaanxi Key Laboratory of Clothing Intelligence, State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.
Baocang WangState Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China.
Xi'an Polytechnic University · CNXidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost.

Indexed as

Aggregate SignatureBlockchainCOVID-19Privacy-preserving contact tracingZero-knowledge proof

Identifiers

PMID35400843
PMCPMC8979615
OpenAlexW4223420444

What OpenQuestion holds

Textmetadata
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.