A Novel Geographic Partitioning System for Anonymizing Health Care Data

With large volumes of detailed health care data being collected, there is a high demand for the release of this data for research purposes. Hospitals and organizations are faced with conicting interests of releasing

[1]  Maged N Kamel Boulos,et al.  International Journal of Health Geographics Open Access towards Evidence-based, Gis-driven National Spatial Health Information Infrastructure and Surveillance Services in the United Kingdom , 2022 .

[2]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Jean-Pierre Corriveau,et al.  A globally optimal k-anonymity method for the de-identification of health data. , 2009, Journal of the American Medical Informatics Association : JAMIA.

[4]  Kenneth D Mandl,et al.  Privacy protection versus cluster detection in spatial epidemiology. , 2006, American journal of public health.

[5]  Khaled El Emam,et al.  A method for managing re-identification risk from small geographic areas in Canada , 2010, BMC Medical Informatics Decis. Mak..

[6]  Bradley Malin,et al.  Evaluating re-identification risks with respect to the HIPAA privacy rule , 2010, J. Am. Medical Informatics Assoc..

[7]  C Nøhr,et al.  A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies , 2014, Yearbook of Medical Informatics.

[8]  Benjamin C. M. Fung,et al.  Anonymizing healthcare data: a case study on the blood transfusion service , 2009, KDD.

[9]  Nitesh Kumar,et al.  Achieving k-anonymity Using Improved Greedy Heuristics for Very Large Relational Databases , 2013, Trans. Data Priv..

[10]  Khaled El Emam,et al.  Model Formulation: Evaluating Predictors of Geographic Area Population Size Cut-offs to Manage Re-identification Risk , 2009, J. Am. Medical Informatics Assoc..

[11]  Roberto J. Bayardo,et al.  Data privacy through optimal k-anonymization , 2005, 21st International Conference on Data Engineering (ICDE'05).

[12]  Geoffrey C. Bowker,et al.  Promoting Access to Public Research Data for Scientific, Economic, and Social Development , 2004, Data Sci. J..

[13]  Latanya Sweeney,et al.  Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[14]  Ho-Won Jung,et al.  A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes , 2014, International Journal of Health Geographics.

[15]  Julian Padget,et al.  Using software agents to preserve individual health data confidentiality in micro-scale geographical analyses , 2006, J. Biomed. Informatics.

[16]  David L. Buckeridge,et al.  The re-identification risk of Canadians from longitudinal demographics , 2011, BMC Medical Informatics Decis. Mak..

[17]  K. Emam,et al.  Evaluating the Risk of Re-identification of Patients from Hospital Prescription Records. , 2009, The Canadian journal of hospital pharmacy.

[18]  Tamir Tassa,et al.  k-Anonymization with Minimal Loss of Information , 2009, IEEE Transactions on Knowledge and Data Engineering.

[19]  Kokichi Sugihara,et al.  Why Are Voronoi Diagrams so Fruitful in Application? , 2011, 2011 Eighth International Symposium on Voronoi Diagrams in Science and Engineering.

[20]  Graham Dunn,et al.  Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary , 2007, Journal of Epidemiology and Community Health.

[21]  Steven Fortune,et al.  A sweepline algorithm for Voronoi diagrams , 1986, SCG '86.

[22]  Khaled El Emam,et al.  Evaluating the risk of patient re-identification from adverse drug event reports , 2013, BMC Medical Informatics and Decision Making.

[23]  C. Skinner,et al.  Safe data versus safe setting: access to microdata from the British Census , 1994 .

[24]  Lowrance Wm.  Access to Collections of Data and Material for Health Research. A report to the Medical Research Council and the Wellcome Trust , 2006 .

[25]  D. Cummings,et al.  The impact of a physical geographic barrier on the dynamics of measles , 2007, Epidemiology and Infection.

[26]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[27]  B. Greenberg,et al.  RELATING RISK OF DISCLOSURE FOR MICRODATA AND GEOGRAPHIC AREA SIZE , 2002 .

[28]  S. Hawala.  Enhancing the " 100 , 000 rule " On The Variation Of The Per Cent Of Uniques In A Microdata Sample And The Geographic Area Size Identified , 2001 .