Abstract
With the development of Electronic Medical Records (EMRs) and Electronic Health Records(EHRs) huge amounts of Personal Health Information (PHI) are now being available and consequently demands for accessing and secondary use of such PHI are increasing. Despite its benefits, the use of PHI for secondary purposes has increased privacy concerns among public due to potential privacy risks arising from improper release and usage of person-specific health data [1]. To address these concerns, governments and ethics boards regulated a set of privacy policies for disclosing (identifiable) personal health data which requires that either consent of patients to be obtained or data to be de-identified before publication[2]. However, as obtaining consent is often not practical in secondary use contexts, data de-identification becomes a better -and sometimes the only- practical approach.
This work is part of a research project at the Electronic Health Information Lab, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kulynych, J., Korn, D.: The effect of the new federal medical privacy rule on research. The New England Journal of Medicine 346(3), 201–204 (2002)
Willison, D., Emerson, C., et al.: Access to medical records for research purposes: Varying perceptions across research ethics boards. Journal of Medical Ethics 34, 308–314 (2008)
Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey on recent developments. ACM Computing Surveys 42(4) (December 2010) (impact factor 9.92 (2009))
El Emam, K., Dankar, F.K., et al.: A globally optimal k-anonymity method for the de-identification of health data. JAMIA 16, 670–682 (2009)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data 1(1) (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sehatkar, M. (2010). Privacy Preserving Publication of Longitudinal Health Data. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-13059-5_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
eBook Packages: Computer ScienceComputer Science (R0)