Abstract
In this paper, we propose a novel preference-constrained approach to k-anonymisation. In contrast to the existing works on k-anonymisation which attempt to satisfy a minimum level of protection requirement as a constraint and then optimise data utility within that constraint, we allow data owners and users to specify their detailed protection and usage requirements as a set of preferences on attributes or data values, treat such preferences as constraints and solve them as a multi-objective optimisation problem. This ensures that anonymised data will be actually useful to data users in their applications and sufficiently protected for data owners. Our preliminary experiments show that our method is capable of producing anonymisations that satisfy a range of preferences and have a high level of data utility and protection.
This is a preview of subscription content, log in via an institution to check access.
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
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. on Knowledge and Data Engineering 13(9), 1010–1027 (2001)
Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: KDD 2003, pp. 206–215 (2003)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: KDD 2002, pp. 217–228 (2002)
Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N.: Fast data anonymization with low information loss. In: VLDB 2007, pp. 758–769 (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: ICDE 2006, p. 24 (2006)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE 2007, pp. 106–115 (2007)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: SIGMOD 2006, pp. 229–240 (2006)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. on Knowledge and Data Engineering 14, 189–201 (2002)
Xu, J., Wang, W., Pei, J., Wang, X., Shi, B., Fu, A.: Utility-based anonymization using local recoding. In: KDD 2006, pp. 785–790 (2006)
Loukides, G., Shao, J.: Data utility and privacy protection trade-off in k-anonymisation. In: EDBT workshop on Privacy and Anonymity in the Information Society, PAIS 2008 (2008)
Koudas, N., Zhang, Q., Srivastava, D., Yu, T.: Aggregate query answering on anonymized tables. In: ICDE 2007, pp. 116–125 (2007)
Loukides, G., Shao, J.: Capturing data usefulness and privacy protection in k-anonymisation. In: SAC 2007, pp. 370–374 (2007)
Wong, R., Li, J., Fu, A., Wang, K.: alpha-k-anonymity: An enhanced k-anonymity model for privacy-preserving data publishing. In: KDD 2006, pp. 754–759 (2006)
Wong, R.C., Fu, A.W., Wang, K., Pei, J.: Minimality attack in privacy preserving data publishing. In: VLDB 2007, pp. 543–554 (2007)
Zhang, L., Jajodia, S., Brodsky, A.: Information disclosure under realistic assumptions: privacy versus optimality. In: CCS 2007, pp. 573–583 (2007)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Coello, C.A.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl. Inf. Syst. 1(3), 129–156 (1999)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: KDD 2002, pp. 279–288 (2002)
Booker, L.: Improving Search in Genetic Algorithms. Morgan Kaufmann, San Francisco (1987)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: SIGMOD 2005, pp. 49–60 (2005)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian multidimensional k-anonymity. In: ICDE 2006, p. 25 (2006)
Hettich, S., Merz, C.J.: Uci repository of machine learning databases (1998)
Li, J., Tao, Y., Xiao, X.: Preservation of proximity privacy in publishing numerical sensitive data. In: SIGMOD 2008, pp. 473–486 (2008)
Wang, K., Fung, B.C.M., Yu, P.S.: Handicapping attacker’s confidence: an alternative to k-anonymization. Knowl. Inf. Syst. 11(3), 345–368 (2007)
Chen, B., Ramakrishnan, R., LeFevre, K.: Privacy skyline: Privacy with multidimensional adversarial knowledge. In: VLDB 2007, pp. 770–781 (2007)
Du, W., Teng, Z., Zhu, Z.: Privacy-maxent: integrating background knowledge in privacy quantification. In: SIGMOD 2008, pp. 459–472 (2008)
Agrawal, R., Wimmers, E.L.: A framework for expressing and combining preferences. In: SIGMOD 2000, pp. 297–306 (2000)
Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation. In: ICDE 2005, pp. 205–216 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Loukides, G., Tziatzios, A., Shao, J. (2009). Towards Preference-Constrained k-Anonymisation. In: Chen, L., Liu, C., Liu, Q., Deng, K. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04205-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-04205-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04204-1
Online ISBN: 978-3-642-04205-8
eBook Packages: Computer ScienceComputer Science (R0)