Abstract
Although many algorithms have been developed to achieve privacy preserving data publishing, few of them can handle incomplete microdata. In this paper, we first show that traditional algorithms based on suppression and generalization cause huge information loss on incomplete microdata. Then, we propose AIM (anatomy for incomplete microdata), a linear-time algorithm based on anatomy, aiming to retain more information in incomplete microdata. Different from previous algorithms, AIM treats missing values as normal value, which greatly reduce the number of records being suppressed. Compared to anatomy, AIM supports more kinds of datasets, by employing a new residue-assignment mechanism, and is applicable to all privacy principles. Results of extensive experiments based on real datasets show that AIM provides highly accurate aggregate information for the incomplete microdata.
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References
Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
Pierangela Samarati, L.S.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. In: IEEE Symposium on Research in Security and Privacy (1998)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3 (2007)
Xiao, X., Tao, Y.: Anatomy: simple and effective privacy preservation. In: Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB 2006, pp. 139–150 (2006)
Xiao, X., Yi, K., Tao, Y.: The hardness and approximation algorithms for l-diversity. In: Proceedings of the 13th International Conference on Extending Database Technology (EDBT), New York, NY, USA, pp. 135–146 (2010)
Aggarwal, C.C.: On k-anonymity and the curse of dimensionality. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB 2005, pp. 901–909 (2005)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data (SIGMOD), New York, NY, USA, pp. 229–240 (2006)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 571–588 (2002)
Samarati, P.: Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data(SIGMOD), New York, NY, USA, pp. 49–60 (2005)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(SIGKDD), New York, NY, USA, pp. 279–288 (2002)
Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of International Conference on Data Engineering (ICDE), Los Alamitos, CA, USA, pp. 217–228 (2005)
Tao, Y., Chen, H., Xiao, X., Zhou, S., Zhang, D.: Angel: Enhancing the utility of generalization for privacy preserving publication. IEEE Transactions on Knowledge and Data Engineering 21(7), 1073–1087 (2009)
Aggarwal, C.C., Yu, P.S.: A Condensation Approach to Privacy Preserving Data Mining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004)
Truta, T.M., Vinay, B.: Privacy protection: p-sensitive k-anonymity property. In: Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW), Washington, DC, USA, p. 94 (2006)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: Proceedings of International Conference on Data Engineering (ICDE), pp. 106–115 (2007)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS), New York, NY, USA, pp. 223–228 (2004)
Yao, C., Wang, X.S., Jajodia, S.: Checking for k-anonymity violation by views. In: Proceedings of International Conference on Very Large Data Bases (VLDB), pp. 910–921 (2005)
Ghinita, G., Kalnis, P., Tao, Y.: Anonymous publication of sensitive transactional data. IEEE Transactions on Knowledge and Data Engineering 23(2), 161–174 (2011)
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Gong, Q., Luo, J., Yang, M. (2013). AIM: A New Privacy Preservation Algorithm for Incomplete Microdata Based on Anatomy. In: Zu, Q., Hu, B., Elçi, A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37015-1_16
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DOI: https://doi.org/10.1007/978-3-642-37015-1_16
Publisher Name: Springer, Berlin, Heidelberg
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