Paper Info
Title
Learning-Based Privacy-Preserving Location Sharing.
Abstract
With the improvement of mobile communication technology, mobile Online Social Networks (mOSNs) provide users with the corresponding location based services when compared with traditional social networks. Location sharing becomes a fundamental component of mOSNs now, and some practical methods and techniques have been proposed to protect user's privacy information. Some of these methods can accommodate privacy protection based on the input user profile and user's privacy preferences through personalization, but user may be unlikely to use them without easy operation and strong privacy guarantee. In this article, we make a further research on privacy-preserving location sharing in mOSNs and develop a framework to help user to choose his desired degree of the privacy protection based on context aware. An adaptive learning model is established to provide user privacy right decisions, based on analyzing a series of factors that influence the choice of user's privacy profile. This model will manage the different contexts of different user privacy preference with minimal user intervention and can achieve self-perfection gradually. So our proposed model can effectively protect users' privacy and motivate users to make use of privacy preferences available to them.
Year
DOI
Venue
2015
10.1007/978-981-10-0356-1_70
Communications in Computer and Information Science
Keywords
Field
DocType
Mobile online social networks,Privacy-preserving,Location sharing,Adaptive learning model
Internet privacy,User profile,Social network,Computer science,Location-based service,Location sharing,Adaptive learning,Mobile telephony,User privacy,Personalization
Conference
Volume
ISSN
Citations 
575
1865-0929
0
PageRank 
References 
Authors
0.34
12
6
Name
Order
Citations
PageRank
Nan Shen1111.58
Xuan Chen200.34
Shuang Liang378.41
Jun Yang4471.95
Tong Li518511.93
Chunfu Jia660245.16