Evidence map›Paper›PMID 27722976›Full record

ArticleJournal of medical systems2016

An Efficient Searchable Encryption Against Keyword Guessing Attacks for Sharable Electronic Medical Records in Cloud-based System.

Yilun Wu, Xicheng Lu, Jinshu Su, Peixin Chen

Abstract read
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In one paragraph

Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018
    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

4 authors.

Yilun WuCollege of Computer, National University of Defense Technology, Changsha, Hunan, People's Republic of China. yl.wu@nudt.edu.cn.ORCID http://orcid.org/0000-0003-1434-2039
Xicheng LuNational Key Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, People's Republic of China.
Jinshu SuNational Key Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, People's Republic of China.
Peixin ChenCollege of Computer, National University of Defense Technology, Changsha, Hunan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preserving the privacy of electronic medical records (EMRs) is extremely important especially when medical systems adopt cloud services to store patients' electronic medical records. Considering both the privacy and the utilization of EMRs, some medical systems apply searchable encryption to encrypt EMRs and enable authorized users to search over these encrypted records. Since individuals would like to share their EMRs with multiple persons, how to design an efficient searchable encryption for sharable EMRs is still a very challenge work. In this paper, we propose a cost-efficient secure channel free searchable encryption (SCF-PEKS) scheme for sharable EMRs. Comparing with existing SCF-PEKS solutions, our scheme reduces the storage overhead and achieves better computation performance. Moreover, our scheme can guard against keyword guessing attack, which is neglected by most of the existing schemes. Finally, we implement both our scheme and a latest medical-based scheme to evaluate the performance. The evaluation results show that our scheme performs much better performance than the latest one for sharable EMRs.

Indexed as

AlgorithmsHealth Information ExchangeCloud ComputingComputer SecurityConfidentialityCosts and Cost AnalysisElectronic Health RecordsCloud storageElectronic medical recordPrivacySearchable encryptionSecure channelsSecurity

Identifiers

What OpenQuestion holds

Textmetadata
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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.