DocumentCode
685974
Title
Privacy Preserving distributed structure learning of probabilistic graphical models
Author
Husheng Li
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA
fYear
2013
fDate
9-13 Dec. 2013
Firstpage
188
Lastpage
193
Abstract
Privacy preserving structure learning of probabilistic graphical model is studied using the framework of secure multi-party computation. Both constraint and score based learning procedures are rendered the capability of privacy preserving. A data set of adolescent health is used to learn the relationships related to drinking behaviors.
Keywords
data privacy; distributed processing; health care; learning (artificial intelligence); probability; adolescent health; constraint procedure; drinking behaviors; privacy preserving distributed structure learning; probabilistic graphical models; score based learning procedure; secure multiparty computation; Bayes methods; Conferences; Data privacy; Graphical models; Privacy; Random variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Globecom Workshops (GC Wkshps), 2013 IEEE
Conference_Location
Atlanta, GA
Type
conf
DOI
10.1109/GLOCOMW.2013.6824984
Filename
6824984
Link To Document