Abstract
Privacy-preserving data mining is gaining prominence due to increased accumulation of data containing personal information. Data holders in healthcare, finance and other sectors collecting person-specific information are challenged to publish useful data, while meeting ever-increasing demands of privacy protection for data subjects. K-anonymity is a popular technique used to preserve data privacy for data publishing by anonymizing quasi identifiers (QI) (e.g., race, gender, age). However, K-anonymized data can be at risk of temporal attacks that target multiple versions of released data, also called sequential releases. The objective of this study is to develop a model that uses multi-class and multi-label classifiers to evaluate risk in re-identifying QI information in previous data releases through learning from current data release. In our empirical study, we use five healthcare and financial data sets to compare performance of binary relationship and label powerset problem transformations and Naïve Bayes, C4.5, random tree and kNN learning algorithms. Our empirical results show that multi-label classification is a powerful tool in enhancing K-anonymity of sequential data release. Statistical analysis of the classification results shows that RAkEL outperforms other transformation methods in predicting demographics information, hence, can be useful in assessing risks of QI re-identification.
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References
Aggarwal, C.: Privacy-preserving data mining.’ In: Data Mining, pp. 663–693. Springer International Publishing (2015)
Cotha, N., Sokolova, M.: Multi-label learning in classification of patients’ quasi-identifiers. Prog. Artificial Intell. 4(3–4), 37–48 (2015)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Dong, Y., Yang, Y., Tang, J., Yang, Y., Chawla, N.: Inferring user demographics and social strategies in mobile social networks. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 2014, pp. 15–24 (2014)
Elisseeff, A., Weston, J.: A Kernel method for multi-labelled classification. In: Proceedings of the Annual ACM Conference on Research and Development in Information Retrieval, pp. 274–281 (2005)
Eze, B., Peyton, L.: Systematic literature review on the anonymization of high dimensional streaming datasets for health data sharing. Proc. Comput. Sci. 63, 348–355 (2015)
Fan, W., Wang, H., Yu, P., Ma, S.: Is random model better? On its accuracy and efficiency. In: Third IEEE International Conference on Data Mining, 2003. ICDM 2003, pp. 51–58. IEEE (2003)
Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Int. Rev. Data Min. Knowl. Disc., 4, 6, pp. 411–444 (2014)
Hu, J., Zeng, H., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: Proceedings of the \(16^{th}\) international conference on World Wide Web, pp. 151–160 (2007)
Jafer, Y., Matwin, S., Sokolova, M.: Task oriented privacy preserving data publishing using feature selection. In: Advances in Artificial Intelligence 27, pp. 143–154. Springer (2014)
Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, Cambridge (2011)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recognit. 45(9), 3084–3104 (2012)
Martínez, S., Sánchez, D., Valls, A.: A semantic framework to protect the privacy of electronic health records with non-numerical attributes. J. Biomed. Inform. 46(2), 294–303 (2013)
Office for Civil Rights, H.: Standards for privacy of individually identifiable health information. Final rule. Federal Register 67(157), 53181 (2002)
Pei, J., Xu, J., Wang, Z., Wang, W., Wang, K.: Maintaining k-anonymity against incremental updates. In: Proceedings of the International Conference on Scientific and Statistical Database Management (2007)
Read, J.: A pruned problem transformation method for multi-label classification. In: Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008), pp. 143–150 (2008)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Proceedings of the 20th European Conference on Machine Learning, pp. 254–269 (2009)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45, 427–437 (2009)
Soria-Comas, J., Domingo-Ferrer, J.: Big data privacy: challenges to privacy principles and models. Data Sci. Eng. 1(1), 21–28 (2016)
Sorower, M.S.: A Literature Survey on Algorithms for Multi-Label Learning. Oregon State University, Corvallis (2010)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 571–588 (2002)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Proceedings of the 18th European Conference on Machine Learning (ECML 2007) (2007)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2007)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2009)
Wang, K., Fung, B.: Anonymizing sequential releases. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 414–423. ACM (2006)
Zhang, X., Yuan, Q., Zhao, S., Fan, W., Zheng, W., Wang, Z.: Multilabel classification without the multi-label cost. In: Proceedings of SDM, pp. 778–789 (2010)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Knowl. Data Eng. Trans. 26(8), 1819–1837 (2014)
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We thank Nathalie Japkowicz for fruitful suggestions on an early study. We thank anonymous reviewers for helpful comments.
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Tran, D., Sokolova, M. Applying multi-label and multi-class classification to enhance K-anonymity in sequential releases. Prog Artif Intell 5, 277–288 (2016). https://doi.org/10.1007/s13748-016-0096-y
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DOI: https://doi.org/10.1007/s13748-016-0096-y