Abstract
This chapter provides a summary of the main topics and methods that have been covered in the book, and it draws inferences about various important aspects of medical data privacy. In particular, it discusses issues and techniques related to preserving privacy in: (1) data sharing, (2) distributed and dynamic settings, and (3) emerging applications. Furthermore, it provides an overview of key legal frameworks for the protection of Personal Health Information (PHI) and of techniques required to comply with these frameworks, such as text de-identification and data governance. Moreover, the chapter discusses some promising directions in the field of medical data privacy.
Keywords
- Medical Data
- Health Information Exchange
- Role Base Access Control
- Personal Health Information
- Differential Privacy
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dean, J., Ghemawat, S.: Mapreduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2010)
Dwork, C.: Differential privacy. In: ICALP, pp. 1–12 (2006)
Emam, K.E., Jonker, E., Arbuckle, L., Malin, B.: A systematic review of re-identification attacks on health data. PLoS ONE 6(12), e28071 (2011)
Gkoulalas-Divanis, A., Loukides, G., Sun, J.: Publishing data from electronic health records while preserving privacy: a survey of algorithms. J. Biomed. Inform. 50, 4–19 (2014)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE, pp. 106–115 (2007)
Nergiz, M.E., Atzori, M., Clifton, C.: Hiding the presence of individuals from shared databases. In: SIGMOD, pp. 665–676 (2007)
Poulis, G., Loukides, G., Gkoulalas-Divanis, A., Skiadopoulos, S.: Anonymizing data with relational and transaction attributes. In: ECML/PKDD, pp. 353–369 (2013)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)
Tamersoy, A., Loukides, G., Nergiz, M.E., Saygin, Y., Malin, B.: Anonymization of longitudinal electronic medical records. IEEE Trans. Inf. Technol. Biomed. 16(3), 413–423 (2012)
U.S. Department of Health and Human Services.: Breaches affecting 500 or more individuals. http://www.hhs.gov/ocr/privacy/hipaa/administrative/breachnotificationrule/index.html (2015). Accessed 6 Sept 2015
Zhang, X., Yang, C., Nepal, S., Chang, L., Dou, W., Chen, J.: A mapreduce based approach of scalable multidimensional anonymization for big data privacy preservation on cloud. In: Cloud and Green Computing, pp. 105–112 (2013)
Zhang, X., Liu, C., Nepal, S., Yang, C., Dou, W., Chen, J.: A hybrid approach for scalable sub-tree anonymization over big data using mapreduce on cloud. J. Comput. Syst. Sci. 80(5), 1008–1020 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gkoulalas-Divanis, A., Loukides, G. (2015). Epilogue. In: Gkoulalas-Divanis, A., Loukides, G. (eds) Medical Data Privacy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-319-23633-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-23633-9_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23632-2
Online ISBN: 978-3-319-23633-9
eBook Packages: Computer ScienceComputer Science (R0)