DocumentCode :
2776761
Title :
Inferring Profile Elements from Publicly Available Social Network Data
Author :
Kozikowski, Piotr ; Groh, Georg
Author_Institution :
Rapleaf Inc., San Francisco, CA, USA
fYear :
2011
fDate :
9-11 Oct. 2011
Firstpage :
876
Lastpage :
881
Abstract :
We investigate methods for inferring attribute values from publicly available profile from social networking platforms. These methods are not intended to attack the privacy of specific users but are intended to be usable on large datasets that can be used for large scale data-mining. We discuss attribute specific methods and put a special focus on methods using the friend-network of a user, either by weighting or selecting relations according to sub-network density.
Keywords :
data mining; social networking (online); attribute values; inferring profile elements; large scale data mining; publicly available profile; social network data; Accuracy; Communities; Data privacy; Educational institutions; Electronic mail; Social network services; Tin; Large Scale Data-Mining; Local Social Network Based Weighted Attribute Inference; Profile Attribute Inference; Social Networking Privacy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-1931-8
Type :
conf
DOI :
10.1109/PASSAT/SocialCom.2011.38
Filename :
6113233
Link To Document :
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