Evidence map›Paper›PMID 28213343›Full record

ArticleJMIR medical informatics2017

Checking Questionable Entry of Personally Identifiable Information Encrypted by One-Way Hash Transformation.

Xianlai Chen, Yang C Fann, Matthew McAuliffe, David Vismer, Rong Yang

Abstract read
In one paragraph

Article in JMIR medical informatics, 2017. 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.

Xianlai ChenInstitute of Information Security and Big Data, Central South University, Changsha, China.ORCID http://orcid.org/0000-0002-4338-015X
Yang C FannIntramural IT and Bioinformatics Program, Division of Intramural, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.ORCID http://orcid.org/0000-0003-2636-2002
Matthew McAuliffeDivision of Computational Science, Center for Information Technology, National Institutes of Health, Bethesda, MD, United States.ORCID http://orcid.org/0000-0002-2409-5126
David VismerSapient Government Services, Arlington, VA, United States.ORCID http://orcid.org/0000-0002-1533-8683
Rong Yang7th Ward, Xiangya Hospital, Central South University, Changsha, China.ORCID http://orcid.org/0000-0003-1433-9606

Funding

Scientific Applications Services SectionZIACT000272 · CIT · CENTER FOR INFORMATION TECHNOLOGY · PI MCAULIFFE, MATTHEW · 2009 to 2021
$9.9M
6 · The paper itself

Abstract

backgroundAs one of the several effective solutions for personal privacy protection, a global unique identifier (GUID) is linked with hash codes that are generated from combinations of personally identifiable information (PII) by a one-way hash algorithm. On the GUID server, no PII is permitted to be stored, and only GUID and hash codes are allowed. The quality of PII entry is critical to the GUID system.

objectiveThe goal of our study was to explore a method of checking questionable entry of PII in this context without using or sending any portion of PII while registering a subject.

methodsAccording to the principle of GUID system, all possible combination patterns of PII fields were analyzed and used to generate hash codes, which were stored on the GUID server. Based on the matching rules of the GUID system, an error-checking algorithm was developed using set theory to check PII entry errors. We selected 200,000 simulated individuals with randomly-planted errors to evaluate the proposed algorithm. These errors were placed in the required PII fields or optional PII fields. The performance of the proposed algorithm was also tested in the registering system of study subjects.

resultsThere are 127,700 error-planted subjects, of which 114,464 (89.64%) can still be identified as the previous one and remaining 13,236 (10.36%, 13,236/127,700) are discriminated as new subjects. As expected, 100% of nonidentified subjects had errors within the required PII fields. The possibility that a subject is identified is related to the count and the type of incorrect PII field. For all identified subjects, their errors can be found by the proposed algorithm. The scope of questionable PII fields is also associated with the count and the type of the incorrect PII field. The best situation is to precisely find the exact incorrect PII fields, and the worst situation is to shrink the questionable scope only to a set of 13 PII fields. In the application, the proposed algorithm can give a hint of questionable PII entry and perform as an effective tool.

conclusionsThe GUID system has high error tolerance and may correctly identify and associate a subject even with few PII field errors. Correct data entry, especially required PII fields, is critical to avoiding false splits. In the context of one-way hash transformation, the questionable input of PII may be identified by applying set theory operators based on the hash codes. The count and the type of incorrect PII fields play an important role in identifying a subject and locating questionable PII fields.

Indexed as

computer securityconfidentialitydata accuracymedical record linkagepersonally identifiable informationprivacyquality controlregistries

Identifiers

PMID28213343
PMCPMC5336604

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LicenceCC BY
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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.