Protecting User Privacy with Social Media Data and Mining

منو

" Protecting User Privacy with Social Media Data and Mining "
Beigi, Ghazaleh Liu, Huan

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1114458
Doc. No	:	TLpq2408208182
Main Entry	:	Beigi, Ghazaleh
	:	Liu, Huan
Title & Author	:	Protecting User Privacy with Social Media Data and Mining\ Beigi, GhazalehLiu, Huan
College	:	Arizona State University
Date	:	2020
student score	:	2020
Degree	:	Ph.D.
Page No	:	169
Abstract	:	The pervasive use of the Web has connected billions of people all around the globe and enabled them to obtain information at their fingertips. This results in tremendous amounts of user-generated data which makes users traceable and vulnerable to privacy leakage attacks. In general, there are two types of privacy leakage attacks for user-generated data, i.e., identity disclosure and private-attribute disclosure attacks. These attacks put users at potential risks ranging from persecution by governments to targeted frauds. Therefore, it is necessary for users to be able to safeguard their privacy without leaving their unnecessary traces of online activities. However, privacy protection comes at the cost of utility loss defined as the loss in quality of personalized services users receive. The reason is that this information of traces is crucial for online vendors to provide personalized services and a lack of it would result in deteriorating utility. This leads to a dilemma of privacy and utility.
Subject	:	Artificial intelligence
	:	Computer science
	:	Machine learning
	:	Privacy protection
	:	Social media mining
	:	User behavioral modeling