Abstract.
We introduce a Privacy-aware Wrapper (PW) system which incorporates privacy into the functionality of wrappers. It ensures that privacy gain is achieved through the selected features without significantly impacting the performance of the models if compared with the performance of the original dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
LeFevre, K., DeWitt, D., Ramakrishnan, R.: Workload-aware anonymization. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 277–286. ACM, Philadelphia (2006)
Fung, B.C., Wang, K., Yu, P.S.: Top-Down specialization for information and privacy preservation. In: Proceedings of the 21st International Conference on Data Engineering, pp. 205–216. IEEE Computer Society (2005)
Wang, K., Yu, P.S., Chakraborty, S.: Bottom-up generalization: a data mining solution to privacy protection. In: Proceedings of the Fourth IEEE International Conference on Data Mining, Bringhton, UK, pp. 249–256 (2004)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness. Knowl.-Based Syst. 10(5), 571–588 (2002)
Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of the Twenty Third International Conference on Data Engineering, Istanbul, Turkey, pp. 106–115 (2007)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Jafer, Y., Matwin, S., Sokolova, M.: Task oriented privacy preserving data publishing using feature selection. In: Sokolova, M., van Beek, P. (eds.) Canadian AI 2014. LNCS, vol. 8436, pp. 143–154. Springer, Heidelberg (2014)
Fung, B.C., et al.: Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42(4), 1–53 (2010)
Jafer, Y., Matwin, S., Sokolova, M.: Using Feature Selection to Improve the Utility of Differentially Private Data Publishing. Procedia Computer Science 37, 511–516 (2014)
Jafer, Y., Matwin, S., Sokolova, M.: Privacy-aware filter-based feature selection. In: First IEEE International Workshop on Big Data Security and Privacy (BDSP 2014), Washington DC, USA (2014)
Press, W.H.: Numerical recipes in C: The art of scientific computing, xxii, 735 p. Cambridge University Press, Cambridge (1988)
Hall, M.A.: Correlation-based feature selection for machine learning. The University of Waikato (1999)
Fayyad, U.M., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, pp. 1022–1027 (1993)
UCI repository. http://archive.ics.uci.edu/ml/ (cited 2013)
Keng-Pei, L., Ming-Syan, C.: On the Design and Analysis of the Privacy-Preserving SVM Classifier. IEEE Transactions on Knowledge and Data Engineering 23(11), 1704–1717 (2011)
Flach, P.: Machine Learning The Art and Science of Algorithms that Make Sense of Data, p. 1 online resource (409 p.) digital, PDF file(s). Cambridge University Press, Cambridge (2012)
Machanavajjhala, A., et al.: L-diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering, Atlanta, Georgia, US, p. 24 (2006)
Cormode, G., et al.: Empirical privacy and empirical utility of anonymized data. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW). IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jafer, Y., Matwin, S., Sokolova, M. (2015). Privacy-aware Wrappers. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-18356-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18355-8
Online ISBN: 978-3-319-18356-5
eBook Packages: Computer ScienceComputer Science (R0)