Anonymizing healthcare data: a case study on the blood transfusion service

ABSTRACT

Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public hospitals, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy, together with an anonymization algorithm, to meet the privacy and information requirements in this BTS case. Experiments on the real-life data demonstrate that our anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.

REFERENCES

• 1: Charu C. Aggarwal, On k-anonymity and the curse of dimensionality, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway
• 2: Charu C. Aggarwal , Philip S. Yu, Privacy-Preserving Data Mining: Models and Algorithms, Springer Publishing Company, Incorporated, 2008
• 3: Rakesh Agrawal , Ramakrishnan Srikant, Privacy-preserving data mining, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.439-450, May 15-18, 2000, Dallas, Texas, United States
• 4: 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]
• 5: D. M. Carlisle, M. L. Rodrian, and C. L. Diamond. California inpatient data reporting manual, medical information reporting for california, 5th edition. Technical report, Office of Statewide Health Planning and Development, July 2007.
• 6: C. Dwork. Differential privacy: A survey of results. Theory and Applications of Models of Computation, 2008.
• 7: B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu. Privacy-preserving data publishing: A survey on recent developments. ACM Computing Surveys, 2010.
• 8: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Anonymizing Classification Data for Privacy Preservation, IEEE Transactions on Knowledge and Data Engineering, v.19 n.5, p.711-725, May 2007 [doi>10.1109/TKDE.2007.1015]
• 9: J. Gardner and L. Xiong. An integrated framework for de-identifying heterogeneous data. DKE, 2009.
• 10: Gabriel Ghinita , Yufei Tao , Panos Kalnis, On the Anonymization of Sparse High-Dimensional Data, Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, p.715-724, April 07-12, 2008 [doi>10.1109/ICDE.2008.4497480]
• 11: Vijay S. Iyengar, Transforming data to satisfy privacy constraints, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775089]
• 12: J. Kim and W. Winkler. Masking microdata files. In ASA Section on Survey Research Methods, 1995.
• 13: Kristen LeFevre , David J. DeWitt , Raghu Ramakrishnan, Workload-aware anonymization techniques for large-scale datasets, ACM Transactions on Database Systems (TODS), v.33 n.3, p.1-47, August 2008 [doi>10.1145/1386118.1386123]
• 14: Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , Muthuramakrishnan Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD), v.1 n.1, p.3-es, March 2007 [doi>10.1145/1217299.1217302]
• 15: Noman Mohammed , Benjamin C. M. Fung , Ke Wang , Patrick C. K. Hung, Privacy-preserving data mashup, Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, March 24-26, 2009, Saint Petersburg, Russia [doi>10.1145/1516360.1516388]
• 16: D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz. UCI repository of machine learning databases, 1998.
• 17: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 18: P. Samarati, Protecting Respondents' Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.1010-1027, November 2001 [doi>10.1109/69.971193]
• 19: A. Skowron and C. Rauszer. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory, chapter The discernibility matrices and functions in information systems. 1992.
• 20: 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]
• 21: Manolis Terrovitis , Nikos Mamoulis , Panos Kalnis, Privacy-preserving anonymization of set-valued data, Proceedings of the VLDB Endowment, v.1 n.1, August 2008 [doi>10.1145/1453856.1453874]
• 22: Ke Wang , Benjamin C. M. Fung, Anonymizing sequential releases, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150449]
• 23: Ke Wang , Benjamin C. M. Fung , Philip S. Yu, Handicapping attacker's confidence: an alternative to k-anonymization, Knowledge and Information Systems, v.11 n.3, p.345-368, April 2007 [doi>10.1007/s10115-006-0035-5]
• 24: Raymond Chi-Wing Wong , Jiuyong Li , Ada Wai-Chee Fu , Ke Wang, (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150499]
• 25: Xiaokui Xiao , Yufei Tao, Anatomy: simple and effective privacy preservation, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
• 26: Yabo Xu , Benjamin C. M. Fung , Ke Wang , Ada W. C. Fu , Jian Pei, Publishing Sensitive Transactions for Itemset Utility, Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, p.1109-1114, December 15-19, 2008 [doi>10.1109/ICDM.2008.98]
• 27: Yabo Xu , Ke Wang , Ada Wai-Chee Fu , Philip S. Yu, Anonymizing transaction databases for publication, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1401982]
• 28: Shipeng Yu , Glenn Fung , Romer Rosales , Sriram Krishnan , R. Bharat Rao , Cary Dehing-Oberije , Philippe Lambin, Privacy-preserving cox regression for survival analysis, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1402013]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration
          Subjects: Security, integrity, and protection

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Algorithms, Performance, Security

Keywords:
anonymity, classification, healthcare, privacy