Literature DB >> 10206109

Using Boolean reasoning to anonymize databases.

A Ohrn1, L Ohno-Machado.   

Abstract

This paper investigates how Boolean reasoning can be used to make the records in a database anonymous. In a medical setting, this is of particular interest due to privacy issues and to prevent the possible misuse of confidential information. As electronic medical records and medical data repositories get more common and widespread, the issue of making sensitive data anonymous becomes increasingly important. A theoretically well-founded algorithm is proposed that via cell suppression can be used to make a database anonymous before releasing or sharing it to the outside world. The degree of anonymity can be tailored according to the specific needs of the recipient, and according to the amount of trust we place in the recipient. Furthermore, the required measure of anonymity can be specified as far down as to the individual objects in the database. The algorithm can also be used for anonymization relative to a particular piece of information, effectively blocking deterministic inferences about sensitive database fields.

Mesh:

Year:  1999        PMID: 10206109     DOI: 10.1016/s0933-3657(98)00056-6

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  10 in total

1.  Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance.

Authors:  L Ohno-Machado; S A Vinterbo; S Dreiseitl
Journal:  Proc AMIA Symp       Date:  2001

2.  Disambiguation data: extracting information from anonymized sources.

Authors:  S Dreiseitl; S Vinterbo; L Ohno-Machado
Journal:  Proc AMIA Symp       Date:  2001

3.  Hiding information by cell suppression.

Authors:  S A Vinterbo; L Ohno-Machado; S Dreiseitl
Journal:  Proc AMIA Symp       Date:  2001

4.  Database design to ensure anonymous study of medical errors: a report from the ASIPS Collaborative.

Authors:  Wilson D Pace; Elizabeth W Staton; Gregory S Higgins; Deborah S Main; David R West; Daniel M Harris
Journal:  J Am Med Inform Assoc       Date:  2003-08-04       Impact factor: 4.497

5.  iDASH: integrating data for analysis, anonymization, and sharing.

Authors:  Lucila Ohno-Machado; Vineet Bafna; Aziz A Boxwala; Brian E Chapman; Wendy W Chapman; Kamalika Chaudhuri; Michele E Day; Claudiu Farcas; Nathaniel D Heintzman; Xiaoqian Jiang; Hyeoneui Kim; Jihoon Kim; Michael E Matheny; Frederic S Resnic; Staal A Vinterbo
Journal:  J Am Med Inform Assoc       Date:  2011-11-10       Impact factor: 4.497

6.  Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule.

Authors:  Bradley Malin; Kathleen Benitez; Daniel Masys
Journal:  J Am Med Inform Assoc       Date:  2011 Jan-Feb       Impact factor: 4.497

7.  Pseudonymization of radiology data for research purposes.

Authors:  Rita Noumeir; Alain Lemay; Jean-Marc Lina
Journal:  J Digit Imaging       Date:  2007-09       Impact factor: 4.056

8.  SHARE: system design and case studies for statistical health information release.

Authors:  James Gardner; Li Xiong; Yonghui Xiao; Jingjing Gao; Andrew R Post; Xiaoqian Jiang; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2012-10-11       Impact factor: 4.497

Review 9.  Shared expectations for protection of identifiable health care information: report of a national consensus process.

Authors:  M K Wynia; S S Coughlin; S Alpert; D S Cummins; L L Emanuel
Journal:  J Gen Intern Med       Date:  2001-02       Impact factor: 5.128

Review 10.  Privacy technology to support data sharing for comparative effectiveness research: a systematic review.

Authors:  Xiaoqian Jiang; Anand D Sarwate; Lucila Ohno-Machado
Journal:  Med Care       Date:  2013-08       Impact factor: 2.983

  10 in total

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