Privacy-Preserving information discovery on EHRs

Information Discovery on Electronic Health Records

Volumn , Issue , 2009, Pages 197-225

  Xiong, Li ^a   Gardner, James ^a   Jurczyk, Pawel ^a   Lu, James J ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78650955531     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (80)

1. Sweeney, L. 2002. fc-Anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems 10(5):557–570.
2. Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. 2006. L Diversity: privacy beyond fc-anonymity In Proceedings of the IEEE International Conference on Data Engineering.
3. Truta, T. M., and Vinay, B. 2006. Privacy protection: p-sensitive fc-anonymity property. In Proceedings of the ICDE Worfcshops.
4. Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., and Zhu, A. 2006. Achieving anonymity via clustering. In Proceedings of PODS, pp. 153–162.
5. Li, N., and Li, T. 2007. t–Closeness: privacy beyond fc-anonymity and l-diversity. In Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE).
6. Xiao, X., and Tao, Y. 2007. M-invariance: towards privacy preserving republication of dynamic datasets. In Proceedings of the SIGMOD Conference, pp. 689–700.
7. Martin, D. J., Kifer, D., Machanavajjhala, A., Gehrke, J., and Halpern, J. Y. 2007. Worst-case background knowledge for privacy–preserving data publishing. In Proceedings of the ICDE.
8. Nergiz, M. E., Atzori, M., and Clifton, C. 2007. Hiding the presence of individuals from shared databases. In Proceedings of the ACM SIGMOD.
9. Jajodia, S., and Sandhu, R. 1991. Toward a multilevel secure relational data model. In Proceedings of the ACM SIGMOD.
10. Agrawal, R., Kieman, J., Srikant, R., and Xu, Y. 2002. Hippocratic databases. In Proceedings of the 28th International Conference on Very Large Data Bases.
11. LeFevre, K., Agrawal, R., Ercegovac, V., Ramakrishnan, R., Xu, Y., and DeWitt, D. 2004. Limiting disclosure in Hippocratic databases. In Proceedings of the 30th International Conference on Very Large Data Bases.
12. Agrawal, R., Bird, P., Grandison, T., Kieman, J., Logan, S., and Rjaibi, W. 2005. Extending relational database systems to automatically enforce privacy policies. In Proceedings of the 21st IEEE International Conference on Data Engineering.
13. Byun, J.-W., Bertino, E., and Li, N. 2005. Purpose based access control of complex data for privacy protection. In ACM Symposium on Access Control Models, and Technologies (SACMAT).
14. Byun, J., and Bertino, E. 2006. Micro-views, or on how to protect privacy while enhancing data usability—concept and challenges. SIGMOD Record 35(1): 9–13.
15. Adams, N. R., and Wortman, J. C. 1989. Security-control methods for statistical databases: a comparative study. ACM Computing Surveys 21(4):515–556.
16. Fung, B. C. M., Wang, K., Chen, R., and Yu, P. S. 2010. Privacy-preserving data publishing: a survey on recent developments. ACM Computing Surveys, 42 (4).
17. Iyengar, V. S. 2002. Transforming data to satisfy privacy constraints. In Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data, pp. 279–288.
18. Wang, K., Yu, P. S., and Chakraborty, S. 2004. Bottom–up generalization: a data mining solution to privacy protection. In Proceedings of ICDM.
19. Meyerson, A., and Williams, R. 2004. On the complexity of optimal k-anonymity. In Proceedings of ACM Symposium on Principles of Database Systems, pp. 223–228.
20. Bayardo, R. J., and Agrawal, R. 2005. Data privacy through optimal k-anonymization. In Proceedings of the ICDE 2005.
21. Aggarwal, C. C. 2005. On k-anonymity and the curse of dimensionality. In Proceedings of the 31st International Conference Very Large Data Bases, pp. 901℃909.
22. Fung, B. C. M., Wang, K., and Yu, P. S. 2005. Top-down specialization for information and privacy preservation. In Proceedings of ICDE.
23. Bertino, E., Ooi, B. C., Yang, Y., and Deng, R. H. 2005. Privacy and ownership preserving of outsourced medical data. In Proceedings of the ICDE 2005.
24. Zhong, S., Yang, Z., and Wright, R. N. 2005. Privacy-enhancing k-anonymization of customer data. In Proceedings of PODS, pp. 139–147.
25. LeFevre, K., Dewitt, D., and Ramakrishnan, R. 2005. Incognito: efficient full domain k-anonymity. In Proceedings of the ACM SIGMOD International Conference on Management of Data.
26. LeFevre, K., Dewitt, D., and Ramakrishnan, R. 2006. Mondrian multidimensional k-anonymity. In Proceedings of the IEEE ICDE.
27. LeFevre, K., Dewitt, D., and Ramakrishnan, R. 2006. Workload-aware anony mization. In Proceedings of the ACM SIGKDD.
28. Wang, K., and Fung, B. C. M. 2006. Anonymizing sequential releases. In Proceedings of the ACM SIGKDD.
29. Kifer, D., and Gehrke, J. 2006. Injecting utility into anonymized datasets. In Proceedings of the SIGMOD Conference, pp. 217–228.
30. Xiao, X., and Tao, Y. 2006. Anatomy: simple and effective privacy preservation. In Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 139–150.
31. Zhang, Q., Koudas, N., Srivastava, D., and Yu, T. 2007. Aggregate query answering on anonymized tables. In Proceedings of ICDE.
32. Sweeney, L. 1996. Replacing personally-identifying information in medical records, the scrub system. Journal of the American Informatics Association, 333–337.
33. Sweeney, L. 1997. Guaranteeing anonymity when sharing medical data, the datafly system. In Proceedings of AMIA Annual Fall Symposium.
34. Thomas, S. M., Mamlin, B., Schadow, G., and McDonald, C. 2002. A successful technique for removing names in pathology reports. In Proceedings of the Annual AMIA Symposium, pp. 777–781.
35. Taira, R. K., Bui, A. A., and Kangarloo, H. 2002. Identification of patient name references within medical documents using semantic selectional restrictions. In Proceedings of the AMIA Symposium.
36. Gupta, D., Saul, M., and Gilbertson, J. 2004. Evaluation of a de-identification (De-id) software engine to share pathology reports and clinical documents for research. American Journal of Clinical Pathology 121(2):176–186.
37. Sibanda, T., and Uzuner, O. 2006. Role of local context in de-identification of ungrammatical fragmented text. In Proceedings of the North American Chapter of Association for Computational Linguistics/Human Language Technology.
38. Beckwith, B. A., Mahaadevan, R., Balis, U. J., and Kuo, F. 2006. Development and evaluation of an open source software tool for de-identification of pathology reports. BMC Medical Informatics and Decision Making 42(1):13–35.
39. Agrawal, R., and Srikant, R. 2000. Privacy-preserving data mining. In Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 439–450.
40. Verykios, V. S., Bertino, E., Fovino, I.N., Provenza, L. P., Saygin, Y., and Theodoridis, Y. 2004. State-of-the-art in privacy preserving data mining. ACM SIGMOD Record 33(1):50–57.
41. Bu, S., Lakshmanan, L. V. S., Ng, R. T., and Ramesh, G. 2007. Preservation of patterns and input-output privacy. In Proceedings of the ICDE.
42. Rizvi, S., and Haritsa, J. R. 2002. Maintaining data privacy in association rule mining. In Proceedings of the 28th International Conference on Very Large Data Bases, pp. 682–693.
43. Evfimievski, A. V., Gehrke, J., and Srikant, R. 2003. Limiting privacy breaches in privacy preserving data mining. In Proceedings of PODS, pp. 211–222.
44. Evfimievski, A. V., Srikant, R., Agrawal, R., and Gehrke, J. 2004. Privacy preserving mining of association rules. Information Systems 29(4):343–364.
45. Kargupta, H., Datta, S., Wang, Q., and Sivakumar, K. 2003. On the privacy preserving properties of random data perturbation techniques. In Proceedings of the ICDM.
46. Huang, Z., Du, W., and Chen, B. 2005. Deriving private information from randomized data. In Proceedings of the SIGMOD Conference, pp. 37–48.
47. Teng, Z., and Du, W. 2006. Comparisons of k-anonymization and randomization schemes under linking attacks. In Proceedings of the ICDM.
48. Lindell, Y., and Pinkas, B. 2002. Privacy preserving data mining. Journal of Cryptology 15(3):177–206.
49. Vaidya, J., and Clifton, C. 2002. Privacy preserving association rule mining in vertically partitioned data. In Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining.
50. Kantarcioglu, M., and Clifton, C. 2004. Privacy preserving data mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering 16(9):1026–1037.
51. Kantarcoglu, M., and Vaidya, J. 2003. Privacy preserving naive Bayes classifier for horizontally partitioned data. In Proceedings of the ICDM Worfcshop on Privacy Preserving Data Mining.
52. Vaidya, J., and Clifton, C. 2003. Privacy preserving naive Bayes classifier for vertically partitioned data. In Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining.
53. Yang, Z., Zhong, S., and Wright, R. N. 2005. Privacy-preserving classification of customer data without loss of accuracy. In Proceedings of SIAM SDM.
54. Vaidya, J., and Clifton, C. 2003. Privacy-preserving fc-means clustering over vertically partitioned data. In Proceedings of SIGKDD.
55. Aggarwal, G., Mishra, N., and Pinkas, B. 2004. Secure computation of the fcth ranked element. In Proceedings of the IACR Conference on Eurocrypt.
56. Agrawal, R., Evfimievski, A., and Srikant, R. 2003. Information sharing across private databases. In Proceedings of SIGMOD.
57. Vaidya, J., and Clifton, C. 2005. Privacy-preserving top-k queries. In Proceedings of ICDE.
58. Kantarcioglu, M., and Clifton, C. 2005. Privacy preserving k-NN classifier. In Proceedings of the ICDE.
59. Xiong, L., Chitti, S., and Liu, L. 2005. Top-k queries across multiple private databases. In Proceedings of the 25th International Conference on Distributed Computing Systems (ICDCS 2005).
60. Bhowmick, S. S., Gruenwald, L., Iwaihara, M., and Chatvichienchai, S. 2006. Private-iye: A framework for privacy preserving data integration. In Proceedings of the ICDE Worfcshops.
61. Xiong, L., Chitti, S., and Liu, L. 2007. Mining multiple private databases using a knn classifier. In Proceedings of the ACM Symposium on Applied Computing (SAC), pp. 435–440.
62. Xiong, L., Chitti, S., and Liu, L. 2007. Preserving data privacy for outsourcing data aggregation services. ACM Transactions on Internet Technology (TOIT) 7(3):17.
63. Xiao, X., and Tao, Y. 2006. Personalized privacy preservation. In Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data.
64. Gardner, J., and Xiong, L. 2008. HIDE: an integrated system for health information de-identification. In Proceedings of the IEEE CBMS.
65. Gardner, J., and Xiong, L. 2009. An integrated framework for anonymizing unstructured medical data. Data and Knowledge Engineering (DKE), in press. Available online at doi:10.1016/j.datak.2009.07.006.
66. Ruch, P., Baud, R. H., Rassinoux, A. M., Bouillon, P., and Robert, G. 2000. Medical document anonymization with a semantic lexicon. In Proceedings of the AMIA Symposium.
67. Berman, J. J. 2003. Concept-match medical data scrubbing: how pathology text can be used in research. Archives of Pathology and Laboratory Medicine 127(66):680–686.
68. Douglass, M., Clifford, G. D., Reisner, A., Long, W. J., Moody, G. B., and Mark, R. G. 2005. De-identification algorithm for free-text nursing notes. Computers in Cardiology 32:331–334.
69. Lafferty, J., McCallum, A., and Pereira, F. 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning.
70. Goldwasser, S. 1997. Multi-party computations: past and present. In Proceedings of the ACM Symposium on Principles of Distributed Computing.
71. Goldreich, O. 2001. Secure multi-party computation. Working Draft, Version 1.3.
72. Jiang, W., and Clifton, C. 2006. A secure distributed framework for achieving k-anonymity. The VLDB Journal 15(4):316–333.
73. Zhong, S., Yang, Z., and Wright, R. N. 2005. Privacy-enhancing k-anonymization of customer data. In Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 139–147. New York, NY: ACM Press.
74. Jurczyk, P., and Xiong, L. 2009. Distributed anonymization: achieving anonymity for both data subjects and data providers. In Annual IFIP WG 11.3 Working Conference on Data and Applications Security (DBSec).
75. Aggarwal, C., and Yu, P. S., eds. 2008. Privacy-Preserving Data Mining: Models and Algorithms. New York, NY: Springer.
76. Clifton, C., Kantarcioglu, M., Lin, X., Vaidya, J., and Zhu, M. 2003. Tools for privacy preserving distributed data mining. In Proceedings of SIGKDD Explorations.
77. Wang, K., Fung, B. C. M., and Dong, G. 2005. Integrating private databases for data analysis. In Proceedings of the IEEE ISI.
78. Kargupta, H., Datta, S., Wang, Q., and Sivakumar, K. 2003. On the privacy preserving properties of random data perturbation techniques. In Proceedings of the IEEE International Conference on Data Mining, p. 99, Melbourne, FL, November.
79. Liu, K., Kargupta, H., and Ryan, J. 2006. Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Transactions on Knowledge and Data Engineering 18(1):92–106.
80. Uzuner, O., Szolovits, P., and Kohane, I. 2006. i2b2 Workshop on natural language processing challenges for clinical records. In Fall Symposium of the American Medical Informatics Association (AMIA).