Evidence map›Paper›PMID 36306318›Full record

ArticlePloS one2022

PPTPS: Building privacy-preserving auditable service with traceable timeliness for public cloud storage.

Li Li, Xiao Lan, Mali Chen, Ting Luo, Li Chen, Yangxin Wang, Yumeng Chen

Abstract read
In one paragraph

Article in PloS one, 2022. 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

7 authors.

Li LiSchool of Artificial Intelligence, Chongqing University of Education, Chongqing, China.ORCID 0000-0003-0836-2893
Xiao LanCybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China.
Mali ChenSchool of Mathematics and Big Data, Chongqing University of Education, Chongqing, China.
Ting LuoSchool of Artificial Intelligence, Chongqing University of Education, Chongqing, China.
Li ChenSchool of Artificial Intelligence, Chongqing University of Education, Chongqing, China.
Yangxin WangSchool of Mathematics and Big Data, Chongqing University of Education, Chongqing, China.
Yumeng ChenSchool of Mathematics and Big Data, Chongqing University of Education, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many works are designed to improve efficiency or enhance security and privacy of publicly-auditable cloud storage. However, building timeliness for cloud storage has not been well studied. Few works presented time-sensitive cloud storage and only focused on specific issues, such as the earliest creation time of files or resistance against a procrastinating auditor. Therefore, there leaves an absence of building traceable timeliness for publicly-auditable cloud storage. In this paper, we propose a solution PPTPS to build Privacy-Preserving auditable service with Traceable timeliness for Public cloud Storage. First, we use the security properties of the blockchain to provide a time-stamp for each phase. It enables the timeliness of cloud storage. Second, we construct efficient publicly-verifiable cloud storage. Third, a customized random mask solution is proposed to prevent privacy leakage during the auditing phase. Moreover, we formally proved the security of PPTPS. At last, experimental results demonstrate that PPTPS is economically sound and technically viable.

Indexed as

BlockchainPrivacyCloud ComputingComputer SecurityConfidentialityRecords

Identifiers

PMID36306318
PMCPMC9616220

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.