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NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research
BACKGROUND: A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for lar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Gunther Eysenbach
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704892/ https://www.ncbi.nlm.nih.gov/pubmed/26554419 http://dx.doi.org/10.2196/medinform.4959 |
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author | Zhang, Guo-Qiang Tao, Shiqiang Xing, Guangming Mozes, Jeno Zonjy, Bilal Lhatoo, Samden D Cui, Licong |
author_facet | Zhang, Guo-Qiang Tao, Shiqiang Xing, Guangming Mozes, Jeno Zonjy, Bilal Lhatoo, Samden D Cui, Licong |
author_sort | Zhang, Guo-Qiang |
collection | PubMed |
description | BACKGROUND: A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers. OBJECTIVE: This paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers. METHODS: NHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f (1), f (2), ..., f ( k ). The input for each function f ( i ) has 3 components: a random number r, an integer n, and input data m. The result, f ( i )(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f (1)(r (1), n (1), m (1)), f (2)(r (2), n (2), m (2)), ..., f ( k )(r ( k ), n ( k ), m ( k )). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier. RESULTS: We performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP). CONCLUSIONS: The CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash. |
format | Online Article Text |
id | pubmed-4704892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Gunther Eysenbach |
record_format | MEDLINE/PubMed |
spelling | pubmed-47048922016-01-12 NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research Zhang, Guo-Qiang Tao, Shiqiang Xing, Guangming Mozes, Jeno Zonjy, Bilal Lhatoo, Samden D Cui, Licong JMIR Med Inform Original Paper BACKGROUND: A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers. OBJECTIVE: This paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers. METHODS: NHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f (1), f (2), ..., f ( k ). The input for each function f ( i ) has 3 components: a random number r, an integer n, and input data m. The result, f ( i )(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f (1)(r (1), n (1), m (1)), f (2)(r (2), n (2), m (2)), ..., f ( k )(r ( k ), n ( k ), m ( k )). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier. RESULTS: We performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP). CONCLUSIONS: The CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash. Gunther Eysenbach 2015-11-10 /pmc/articles/PMC4704892/ /pubmed/26554419 http://dx.doi.org/10.2196/medinform.4959 Text en ©Guo-Qiang Zhang, Shiqiang Tao, Guangming Xing, Jeno Mozes, Bilal Zonjy, Samden D Lhatoo, Licong Cui. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 10.11.2015. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Zhang, Guo-Qiang Tao, Shiqiang Xing, Guangming Mozes, Jeno Zonjy, Bilal Lhatoo, Samden D Cui, Licong NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title | NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title_full | NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title_fullStr | NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title_full_unstemmed | NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title_short | NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research |
title_sort | nhash: randomized n-gram hashing for distributed generation of validatable unique study identifiers in multicenter research |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704892/ https://www.ncbi.nlm.nih.gov/pubmed/26554419 http://dx.doi.org/10.2196/medinform.4959 |
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