Title :
Privacy-preserving data publishing
Author :
Liu, Ruilin ; Wang, Hui
Author_Institution :
Comput. Sci. Dept., Stevens Inst. of Technol. Hoboken, Hoboken, NJ, USA
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;
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
DOI :
10.1109/ICDEW.2010.5452722
