Summary
Given a microdata table T, the objective of privacy preserving publication is to release a distorted version T′ of T such that T′ does not allow an adversary to confidently derive the sensitive data of any individual, and yet, T′ can be used to analyze the statistical patterns significant in T. The existing methods of privacy preserving publication is essentially the integration of an anonymization framework and an anonymization principle. Specifically, a framework describes how anonymization is performed, whereas a principle measures whether a suficient amount of anonymization has been applied. In this chapter, we will discuss the characteristics of two existing frameworks: generalization and anatomy, and of two most popular principles: k-anonymity and l-diversity.
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
C. C. Aggarwal. On k-anonymity and the curse of dimensionality. In Proc. of Very Large Data Bases (VLDB), pages 901–909, 2005.
G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu. Anonymizing tables. In Proc. of International Conference on Database Theory (ICDT), pages 246–258, 2005.
R. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In Proc. of International Conference on Data Engineering (ICDE), pages 217–228, 2005.
V. Ciriani, D. C. di Vimercati, S. Foresti, and P. Samarati. k-anonymity. Springer, 2006.
Y. Du, T. Xia, Y. Tao, D. Zhang, and F. Zhu. On multidimensional k-anonymity with local recoding generalization. In Proc. of International Conference on Data Engineering (ICDE), 2007.
B. C. M. Fung, K. Wang, and P. S. Yu. Top-down specialization for information and privacy preservation. In Proc. of International Conference on Data Engineering (ICDE), pages 205–216, 2005.
V. Iyengar. Transforming data to satisfy privacy constraints. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), pages 279–288, 2002.
K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. In Proc. of ACM Management of Data (SIGMOD), pages 49–60, 2005.
K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In Proc. of International Conference on Data Engineering (ICDE), 2006.
N. Li and T. Li. t-closeness: Privacy beyond k-anonymity and l-diversity. In Proc. of International Conference on Data Engineering (ICDE), 2007.
A. Machanavajjhala, J. Gehrke, and D. Kifer. l-diversity: Privacy beyond k-anonymity. In Proc. of International Conference on Data Engineering (ICDE), 2006.
A. Meyerson and R. Williams. On the complexity of optimal k-anonymity. In Proc. of ACM Symposium on Principles of Database Systems (PODS), pages 223–228, 2004.
P. Samarati. Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE), 13(6):1010–1027, 2001.
P. Samarati and L. Sweeney. Generalizing data to provide anonymity when disclosing information. In Proc. of ACM Symposium on Principles of Database Systems (PODS), page 188, 1998.
L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness, and Knowlege-Based Systems, 10(5):557–570, 2002.
N. Thaper, S. Guha, P. Indyk, and N. Koudas. Dynamic multidimensional histograms. In Proc. of ACM Management of Data (SIGMOD), pages 428–439, 2002.
K. Wang, P. S. Yu, and S. Chakraborty. Bottom-up generalization: A data mining solution to privacy protection. In Proc. of International Conference on Management of Data (ICDM), pages 249–256, 2004.
X. Xiao and Y. Tao. Anatomy: Simple and effective privacy preservation. In Proc. of Very Large Data Bases (VLDB), pages 139–150, 2006.
X. Xiao and Y. Tao. Personalized privacy preservation. In Proc. of ACM Management of Data (SIGMOD), pages 229–240, 2006.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC.
About this chapter
Cite this chapter
Tao, Y. (2008). Privacy Preserving Publication: Anonymization Frameworks and Principles. In: Gertz, M., Jajodia, S. (eds) Handbook of Database Security. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-48533-1_20
Download citation
DOI: https://doi.org/10.1007/978-0-387-48533-1_20
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-48532-4
Online ISBN: 978-0-387-48533-1
eBook Packages: Computer ScienceComputer Science (R0)