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 :
بازگشت