DocumentCode
2181562
Title
Privacy-preserving data publishing
Author
Liu, Ruilin ; Wang, Hui
Author_Institution
Comput. Sci. Dept., Stevens Inst. of Technol. Hoboken, Hoboken, NJ, USA
fYear
2010
fDate
1-6 March 2010
Firstpage
305
Lastpage
308
Abstract
Data publishing has generated much concern on individual privacy. Recent work has focused on different background knowledge and their various threats to the privacy of published data. However, there still exist a few types of adversary knowledge waiting to be investigated. In this paper, I explain my research on privacy-preserving data publishing (PPDP) by using full functional dependencies (FFDs) as part of adversary knowledge. I also briefly explain my research plan.
Keywords
data privacy; publishing; set theory; full functional dependencies; privacy preserving data publishing; set theory; Cancer; Computer science; Couplings; Data privacy; Diabetes; Inference algorithms; Intrusion detection; Protection; Publishing; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on
Conference_Location
Long Beach, CA
Print_ISBN
978-1-4244-6522-4
Electronic_ISBN
978-1-4244-6521-7
Type
conf
DOI
10.1109/ICDEW.2010.5452722
Filename
5452722
Link To Document