Abstract
Current privacy preserving methods in data publishing always remove the individually identifying attribute first and then generalize the quasi-identifier attributes. They cannot take the individually identifying attribute into account. In fact, tuples will become vulnerable in the situation of multiple tuples per individual. In this paper, we analyze the individually identifying attribute in the privacy preserving data publishing and propose the concept of identity-reserved anonymity. We develop two approaches to meet identity-reserved anonymity requirement. The algorithms are evaluated in an experimental scenario, demonstrating practical applicability of the approaches.
Chapter PDF
Similar content being viewed by others
References
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing Tables. In: Proceedings of the 10th International Conference on Database Theory, pp. 246–258 (2005)
Bayardo, R., Agrawal, R.: Data privacy through optimal k-anonymization. In: the 21st International Conference on Data Engineering, pp. 217–228 (2005)
Fung, B.C.M., Wang, K., Yu, P.S.: Top-down Specialization for Information and Privacy Preservation. In: the 21st International Conference on Data Engineering, pp. 205–216 (2005)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficient Full-domain K-anonymity. In: ACM International Conference on Management of Data, pp. 49–60 (2005)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian Multidimensional K-Anonymity. In: the 22nd International Conference on Data Engineering, pp. 25–35 (2006)
Li, N., Li, T.: t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: the 23rd International Conference on Data Engineering, pp. 106–115 (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D.: l-diversity: Privacy beyond K-anonymity. In: the 22nd International Conference on Data Engineering, pp. 24–35 (2006)
Samarati, P.: Protecting Respondents’ Identities in Microdata Release. IEEE Transactions on Knowledge and Data Engineering 13, 1010–1027 (2001)
Sweeney, L.: Achieving K-anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness and Knowledge Based Systems 10, 571–588 (2002)
Sweeney, L.: K-anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge Based Systems 10, 557–570 (2002)
UCI Machine Learning Repository, www.ics.uci.edu/~mlearn/MLRepository.html
Wong, R.C., Li, J., Fu, A.W., Wang, K. (α,k)-anonymity: an Enhanced K-anonymity Model for Privacy-preserving Data Publishing. In: the 12th ACM SIGKDD, pp. 754–759 (2006)
Xiao, X., Tao, Y.: Personalized Privacy Preservation. In: ACM International Conference on Management of Data, pp. 229–240 (2006)
Xiao, X., Tao, Y.: Anatomy: Simple and Effective Privacy Preservation. In: the 32nd international conference on Very large data bases, pp. 139–150 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 IFIP International Federation for Information Processing
About this paper
Cite this paper
Tao, Y., Tong, Y., Tan, S., Tang, S., Yang, D. (2008). Protecting the Publishing Identity in Multiple Tuples. In: Atluri, V. (eds) Data and Applications Security XXII. DBSec 2008. Lecture Notes in Computer Science, vol 5094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70567-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-70567-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70566-6
Online ISBN: 978-3-540-70567-3
eBook Packages: Computer ScienceComputer Science (R0)