Unlike conventional methods that exert the same amount of privacy control over all the tuples in the microdata, personalized privacy preservation applies various degrees of protection to different tuples, subject to the preferences of the data owners. This chapter explains the formulation of personal preferences, and the computation of anonymized tables that fulfill the privacy requirement of everybody. Several theoretical results regarding privacy guarantees will also be discussed. Finally, we point out the open research issues that may be explored in the future.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
C. C. Aggarwal and P. S. Yu. A condensation approach to privacy preserving data mining. In EDBT, pages 183–199, 2004.
C. C. Aggarwal and P. S. Yu. On variable constraints in privacy preserving data mining. In SDM, 2005.
R. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In ICDE, pages 217–228, 2005.
B. C. M. Fung, K. Wang, and P. S. Yu. Top-down specialization for information and privacy preservation. In ICDE, pages 205–216, 2005.
B. Gedik and L. Liu. Location privacy in mobile systems: A personalized anonymization model. In ICDCS, pages 620–629, 2005.
M. Gruteser and D. Grunwald. Anonymous usage of location-based services through spatial and temporal cloaking. In MobiSys, 2003.
V. Iyengar. Transforming data to satisfy privacy constraints. In SIGKDD, pages 279–288, 2002.
K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. In SIGMOD, pages 49–60, 2005.
A. Machanavajjhala, J. Gehrke, and D. Kifer. l-diversity: Privacy beyond k-anonymity. In ICDE, 2006.
M. F. Mokbel, C.-Y. Chow, and W. G. Aref. The new casper: Query processing for location services without compromising privacy. In VLDB, pages 763–774, 2006.
P. Samarati. Protecting respondents’ identities in microdata release. TKDE, 13(6):1010–1027, 2001.
L. Sweeney. Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10(5):571–588, 2002.
K. Wang, B. C. M. Fung, and P. S. Yu. Template-based privacy preservation in classification problems. In ICDM, pages 466–473, 2005.
K. Wang, P. S. Yu, and S. Chakraborty. Bottom-up generalization: A data mining solution to privacy protection. In ICDM, pages 249–256, 2004.
X. Xiao and Y. Tao. Personalized privacy preservation. In 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., Xiao, X. (2008). Personalized Privacy Preservation. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_19
Download citation
DOI: https://doi.org/10.1007/978-0-387-70992-5_19
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-70991-8
Online ISBN: 978-0-387-70992-5
eBook Packages: Computer ScienceComputer Science (R0)