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Efficient determination of equivalence for encrypted data.
Doctor, Jason N; Vaidya, Jaideep; Jiang, Xiaoqian; Wang, Shuang; Schilling, Lisa M; Ong, Toan; Matheny, Michael E; Ohno-Machado, Lucila; Meeker, Daniella.
Afiliación
  • Doctor JN; Schaeffer Center for Health Policy and Economics, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-3333, United States.
  • Vaidya J; Management Science & Information Systems Department, Rutgers University, Newark, NJ, United States.
  • Jiang X; UCSD Health Department of Biomedical Informatics, UC San Diego, La Jolla, CA, United States.
  • Wang S; UCSD Health Department of Biomedical Informatics, UC San Diego, La Jolla, CA, United States.
  • Schilling LM; Department of Medicine, University of Colorado, Anschutz Medical Campus, CO, United States.
  • Ong T; Department of Medicine, University of Colorado, Anschutz Medical Campus, CO, United States.
  • Matheny ME; Geriatric Research Education and Clinical Care Service, Tennessee Valley Healthcare System VA, Nashville, TN, United States.
  • Ohno-Machado L; Department of Biomedical Informatics, Medicine, and Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.
  • Meeker D; UCSD Health Department of Biomedical Informatics, UC San Diego, La Jolla, CA, United States.
Comput Secur ; 972020 Oct.
Article en En | MEDLINE | ID: mdl-33223585
ABSTRACT
Secure computation of equivalence has fundamental application in many different areas, including health-care. We study this problem in the context of matching an individual's identity to link medical records across systems under the socialist millionaires'

problem:

Two millionaires wish to determine if their fortunes are equal without disclosing their net worth (Boudot, et al. 2001). In Theorem 2, we show that when a "greater than" algorithm is carried out on a totally ordered set it is easy to achieve secure matching without additional rounds of communication. We present this efficient solution to assess equivalence using a set intersection algorithm designed for "greater than" computation and demonstrate its effectiveness on equivalence of arbitrary data values, as well as demonstrate how it meets regulatory criteria for risk of disclosure.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Comput Secur Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Comput Secur Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos