Abstract
Existing privacy regulations together with large amounts of available data created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model were addressed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α,k)-anonymity, t-closeness. In this chapter we describe two algorithms (GreedyPKClustering and EnhancedPKClustering) for generating (extended) p-sensitive k-anonymous microdata. In our experiments, we compare the quality of generated microdata obtained with the mentioned algorithms and with another existing anonymization algorithm (Incognito). Also, we present two new branches of p-sensitive k-anonymity, the constrained p-sensitive k-anonymity model and the p-sensitive k-anonymity model for social networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
N.R. Adam and J.C. Wortmann, Security Control Methods for Statistical Databases: A Comparative Study, ACM Computing Surveys 21(4) (1989), pp. 515–556.
G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, Anonymizing Tables, in: Proceedings of the International Conference on Database Theory, 2005, pp. 246–258.
R. Agrawal, J. Kiernan, R. Srikant, R. and Y. Xu. Hippocratic Databases, in: Proceedings of the Very Large Data Base Conference, 2002, pp. 143–154.
R.J. Bayardo and R. Agrawal, Data Privacy through Optimal k-Anonymization, in: Proceedings of the IEEE International Conference on Data Engineering, 2005, pp. 217–228.
J.W. Byun, A. Kamra, E. Bertino and N. Li, Efficient k-Anonymity using Clustering Techniques, in: Proceedings of Database Systems for Advanced Applications, 2006, pp. 188–200.
A. Campan and T.M. Truta, Extended P-Sensitive K-Anonymity, Studia Universitatis Babes-Bolyai Informatica 51(2) (2006), pp. 19–30.
A. Campan, T.M. Truta, J. Miller and R.A. Sinca, Clustering Approach for Achieving Data Privacy, in: Proceedings of the International Data Mining Conference, 2007, pp. 321–327.
A. Campan and T.M. Truta, A Clustering Approach for Data and Structural Anonymity in Social Networks, in: Proceedings of the Privacy, Security, and Trust in KDD Workshop, 2008.
D. Lambert, Measures of Disclosure Risk and Harm, Journal of Official Statistics 9 (1993), pp. 313–331.
K. LeFevre, D. DeWitt and R. Ramakrishnan, Incognito: Efficient Full-Domain K-Anonymity, in: Proceedings of the ACM SIGMOD, 2005, pp. 49–60.
K. LeFevre, D. DeWitt and R. Ramakrishnan, Mondrian Multidimensional K-Anonymity, in: Proceedings of the IEEE International Conference on Data Engineering, 2006, 25.
N. Li, T. Li and S. Venkatasubramanian, T-Closeness: Privacy Beyond k-Anonymity and l-Diversity, in: Proceedings of the IEEE International Conference on Data Engineering, 2007, pp. 106–115.
A. Machanavajjhala, J. Gehrke and D. Kifer, L-Diversity: Privacy beyond K-Anonymity, in: Proceedings of the IEEE International Conference on Data Engineering, 2006, 24.
J. Miller, A. Campan and T.M. Truta, Constrained K-Anonymity: Privacy with Generalization Boundaries, in: Proceedings of the Practical Preserving Data Mining Workshop, 2008.
M.C. Mont, S. Pearson and R. Thyne, A Systematic Approach to Privacy Enforcement and Policy Compliance Checking in Enterprises, in: Proceedings of the Trust and Privacy in Digital Business Conference, 2006, pp. 91–102.
MSNBC, Privacy Lost, 2006, Available online at http://www.msnbc.msn.com/id/15157222.
D.J. Newman, S. Hettich, C.L. Blake and C.J. Merz, UCI Repository of Machine Learning Databases, UC Irvine, 1998, Available online at www.ics.uci.edu/mlearn/MLRepository.html.
P. Samarati, Protecting Respondents Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering 13(6) (2001), pp. 1010–1027.
L. Sweeney, k-Anonymity: A Model for Protecting Privacy, International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5) (2002), pp. 557–570.
L. Sweeney, Achieving k-Anonymity Privacy Protection Using Generalization and Suppression, International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5) (2002), pp. 571–588.
T.M. Truta and V. Bindu, Privacy Protection: P-Sensitive K-Anonymity Property, in: Proceedings of the ICDE Workshop on Privacy Data Management, 2006, 94.
T.M. Truta, A. Campan and P. Meyer, Generating Microdata with P-Sensitive K-Anonymity Property, in: Proceedings of the VLDB Workshop on Secure data Management, 2007, pp. 124–141.
L. Willemborg and T. Waal (ed), Elements of Statistical Disclosure Control, Springer Verlag, New York, 2001.
R.C.W. Wong, J. Li, A.W.C. Fu and K. Wang, (α, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy-Preserving Data Publishing, in: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, 2006, pp. 754–759.
R.C.W. Wong, J. Li, A.W.C. Fu and J. Pei, Minimality Attack in Privacy-Preserving Data Publishing, in: Proceedings of the Very Large Data Base Conference, 2007, pp. 543–554.
X. Xiao and Y. Tao, Personalized Privacy Preservation, in: Proceedings of the ACM SIGMOD, 2006, pp. 229–240.
B. Zhou and J. Pei, Preserving Privacy in Social Networks against Neighborhood Attacks, in: Proceedings of the IEEE International Conference on Data Engineering, 2008, pp. 506–515.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Truta, T.M., Campan, A. (2010). Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model. In: Stahlbock, R., Crone, S., Lessmann, S. (eds) Data Mining. Annals of Information Systems, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1280-0_16
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1280-0_16
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-1279-4
Online ISBN: 978-1-4419-1280-0
eBook Packages: Computer ScienceComputer Science (R0)