HIDE: heterogeneous information DE-identification

ABSTRACT

While there is an increasing need to share data that may contain personal information, such data sharing must preserve individual privacy without disclosing any identifiable information. A considerable amount of research in the data privacy community has been devoted to formalizing the notion of identifiability with many techniques for anonymization, but is focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in the medical informatics community are highly specialized for specific document types or a subset of identifiers. In addition, they rely on simple identifier removal or grouping techniques and do not take advantage of the research developments in the data privacy community. We developed an integrated system, HIDE, for Heterogeneous Information DE-identification including structured and unstructured data utilizing existing anonymization techniques. We demonstrate a prototype of our system and show the effectiveness of our approach through a set of real data augmented with synthesized data.

REFERENCES

• 1: R. M. B. A. Beckwith, U. J. Balis, and F. Kuo. Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Medical Informatics and Decision Making, 6(12), 2006.
• 2: Roberto J. Bayardo , Rakesh Agrawal, Data Privacy through Optimal k-Anonymization, Proceedings of the 21st International Conference on Data Engineering, p.217-228, April 05-08, 2005 [doi>10.1109/ICDE.2005.42]
• 3: Elisa Bertino , Beng Chin Ooi , Yanjiang Yang , Robert H. Deng, Privacy and Ownership Preserving of Outsourced Medical Data, Proceedings of the 21st International Conference on Data Engineering, p.521-532, April 05-08, 2005 [doi>10.1109/ICDE.2005.111]
• 4: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Top-Down Specialization for Information and Privacy Preservation, Proceedings of the 21st International Conference on Data Engineering, p.205-216, April 05-08, 2005 [doi>10.1109/ICDE.2005.143]
• 5: James Gardner , Li Xiong, HIDE: An Integrated System for Health Information DE-identification, Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems, p.254-259, June 17-19, 2008 [doi>10.1109/CBMS.2008.129]
• 6: D. Gupta, M. Saul, and J. Gilbertson. Evaluation of a deidentification (de-id) software engine to share pathology reports and clinical documents for research. American Journal of Clinical Pathology, 2004.
• 7: P. Jurczyk, J. J. Lu, L. Xiong, J. D. Cragan, and A. Correa. FRIL: A tool for comparative record linkage. In AMIA Annual Symposium, 2008.
• 8: John D. Lafferty , Andrew McCallum , Fernando C. N. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning, p.282-289, June 28-July 01, 2001
• 9: R. Leaman and G. G. Banner: An executable survey of advances in biomedical named entity recognition. In Pacific Symposium on Biocomputing, 2008.
• 10: Kristen LeFevre , David J. DeWitt , Raghu Ramakrishnan, Mondrian Multidimensional K-Anonymity, Proceedings of the 22nd International Conference on Data Engineering, p.25, April 03-07, 2006 [doi>10.1109/ICDE.2006.101]
• 11: N. Li and T. Li. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, 2007.
• 12: Ashwin Machanavajjhala , Johannes Gehrke , Daniel Kifer , Muthuramakrishnan Venkitasubramaniam, \ell -Diversity: Privacy Beyond \kappa -Anonymity, Proceedings of the 22nd International Conference on Data Engineering, p.24, April 03-07, 2006 [doi>10.1109/ICDE.2006.1]
• 13: A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.
• 14: D. Nadeau and S. Sekine. A survey of named entity recognition and classification. Linguisticae Investigationes, 30(7), 2007.
• 15: Mehmet Ercan Nergiz , Maurizio Atzori , Chris Clifton, Hiding the presence of individuals from shared databases, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247554]
• 16: Tawanda Sibanda , Ozlem Uzuner, Role of local context in automatic deidentification of ungrammatical, fragmented text, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, p.65-73, June 04-09, 2006, New York, New York [doi>10.3115/1220835.1220844]
• 17: L. Sweeney. Replacing personally-identifying information in medical records, the scrub system. Journal of the American Informatics Association, pages 333--337, 1996.
• 18: Latanya Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.557-570, October 2002 [doi>10.1142/S0218488502001648]
• 19: Ke Wang , Philip S. Yu , Sourav Chakraborty, Bottom-Up Generalization: A Data Mining Solution to Privacy Protection, Proceedings of the Fourth IEEE International Conference on Data Mining, p.249-256, November 01-04, 2004
• 20: Xiaokui Xiao , Yufei Tao, M-invariance: towards privacy preserving re-publication of dynamic datasets, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247556]
• 21: Q. Zhang, N. Koudas, D. Srivastava, and T. Yu. Aggregate query answering on anonymized tables. In ICDE, 2007.