Abstract
In this book, we have explained why EMR data need to be disseminated in a way that prevents patient re-identification. We have provided an overview of data sharing policies and regulations, which serve as a first line of defence but are unable to provide computational privacy guarantees, and then reviewed several anonymization approaches that can be used to prevent this threat. Specifically, we have surveyed anonymization principles and algorithms for demographics and diagnosis codes, which are high replicable, available, and distinguishable, and thus may lead to patient re-identification. Anonymity threats and methods for publishing patient information, contained in genomic data, have also been discussed.
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
Chen, K., Liu, L.: Privacy preserving data classification with rotation perturbation. In: ICDM, pp. 589–592 (2005)
Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1–2), 1–24 (2002)
Clifton, C.: Using sample size to limit exposure to data mining. J. of Computer Security 8(4), 281–307 (2000)
Das, G., Zhang, N.: Privacy risks in health databases from aggregate disclosure. In: PETRA, pp. 1–4 (2009)
Emam, K.E.: Methods for the de-identification of electronic health records for genomic research. Genome Medicine 3(4), 25 (2011)
Fienberg, S.E., Slavkovic, A., Uhler, C.: Privacy preserving gwas data sharing. In: IEEE ICDM Worksops, pp. 628–635 (2011)
Gkoulalas-Divanis, A., Loukides, G.: Revisiting sequential pattern hiding to enhance utility. In: KDD, pp. 1316–1324 (2011)
Gkoulalas-Divanis, A., Verykios, V.S.: Exact knowledge hiding through database extension. TKDE 21(5), 699–713 (2009)
Gkoulalas-Divanis, A., Verykios, V.S.: Hiding sensitive knowledge without side effects. KAIS 20(3), 263–299 (2009)
Hall, R., Fienberg, S.E.: Privacy-preserving record linkage. In: Privacy in Statistical Databases, pp. 269–283 (2010)
Hristidis, V.: Information Discovery on Electronic Health Records. Data Mining and Knowledge Discovery. Chapman and Hall/CRC (2009)
Jin, H., Chen, J., He, H., G.Williams, Kelman, C., OKeefe, C.: Mining unexpected temporal associations: Applications in detecting adverse drug reactions. IEEE TITB 12(4), 488500 (2008)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE, pp. 106–115 (2007)
Loukides, G., Gkoulalas-Divanis, A., Malin, B.: An integrative framework for anonymizing clinical and genomic data. In: C. Plant (ed.) Database technology for life sciences and medicine, pp. 65–89. World scientific (2010)
Loukides, G., Gkoulalas-Divanis, A., Malin, B.: COAT: Constraint-based anonymization of transactions. KAIS 28(2), 251–282 (2011)
Loukides, G., Gkoulalas-Divanis, A., Shao, J.: Anonymizing transaction data to eliminate sensitive inferences. In: DEXA, pp. 400–415 (2010)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: ICDE, p. 24 (2006)
Malin, B., Loukides, G., Benitez, K., Clayton, E.: Identifiability in biobanks: models, measures, and mitigation strategies. Human Genetics 130(3), 383–392 (2011)
Moustakides, G.V., Verykios, V.S.: A max-min approach for hiding frequent itemsets. ICDM Workshops pp. 502–506 (2006)
Natwichai, J., Li, X., Orlowska, M.: Hiding classification rules for data sharing with privacy preservation. In: DAWAK, pp. 468–467 (2005)
Nergiz, M.E., Atzori, M., Clifton, C.: Hiding the presence of individuals from shared databases. In: SIGMOD ’07, pp. 665–676 (2007)
Nergiz, M.E., Clifton, C.W.: d-presence without complete world knowledge. TKDE 22(6), 868–883 (2010)
Oliveira, S.R.M., Zaïane, O.R.: Protecting sensitive knowledge by data sanitization. In: ICDM, pp. 613–616 (2003)
Samarati, P.: Protecting respondents identities in microdata release. TKDE 13(9), 1010–1027 (2001)
Saygin, Y., Verykios, V., Clifton, C.: Using unknowns to prevent discovery of association rules. SIGMOD Record 30(4), 45–54 (2001)
Sun, X., Yu, P.S.: A border-based approach for hiding sensitive frequent itemsets. 5th IEEE International Conference on Data Mining p. 8 (2005)
Sweeney, L.: k-anonymity: a model for protecting privacy. IJUFKS 10, 557–570 (2002)
Terrovitis, M., Mamoulis, N., Kalnis, P.: Privacy-preserving anonymization of set-valued data. PVLDB 1(1), 115–125 (2008)
Verykios, V.S., Gkoulalas-Divanis, A.: A Survey of Association Rule Hiding Methods for Privacy, chap. 11, pp. 267–289. Privacy Preserving Data Mining: Models and Algorithms. Springer (2008)
Winkler, W.: Record linkage and bayesian networks. In: Section on Survey Research Methods, American Statistical Association (2002)
Xiao, X., Tao, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: SIGMOD, pp. 689–700 (2007)
Y. Sung, Y., Liu, Y., Xiong, H., Ng, A.: Privacy preservation for data cubes. Knowledge Information Systems 9(1), 38–61 (2006)
Yanqing, J., Hao, Y., Dews, P., Mansour, A., Tran, J., Miller, R., Massanari, R.: A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE TITB 15(3), 428 –437 (2011)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 The Author(s)
About this chapter
Cite this chapter
Gkoulalas-Divanis, A., Loukides, G. (2013). Conclusions and Open Research Challenges. In: Anonymization of Electronic Medical Records to Support Clinical Analysis. SpringerBriefs in Electrical and Computer Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5668-1_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-5668-1_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5667-4
Online ISBN: 978-1-4614-5668-1
eBook Packages: EngineeringEngineering (R0)