Full Citation

Title: Personalized Privacy Preservation

Citation Type: Book, Section

Publication Year: 2008

ISBN: 978-0-387-70992-5; 978-0-387-70991-8

ISSN:

DOI: 10.1007/978-0-387-70992-5_19

NSFID:

PMCID:

PMID:

Abstract: 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.

Url: http://link.springer.com/10.1007/978-0-387-70992-5_19

User Submitted?: No

Authors: Tao, Yufei; Xiao, Xiaokui

Editors: Aggarwal, Charu C.; Yu, Philip S.

Pages: 461-485

Volume Title: Privacy-Preserving Data Mining

Publisher: Springer, Boston, MA

Publisher Location: Boston, MA

Volume:

Edition:

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

Countries: