scholarly journals Safebook: A privacy-preserving online social network leveraging on real-life trust

2009 ◽  
Vol 47 (12) ◽  
pp. 94-101 ◽  
Author(s):  
Leucio Cutillo ◽  
Refik Molva ◽  
Thorsten Strufe
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Privacy Preserving

A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification

2021 ◽  
Vol 11 (2) ◽  
pp. 17-31
Author(s):  
Lanfang Zhang ◽  
Zhiyong Zhang ◽  
Ting Zhao
Keyword(s):  
Social Network ◽  
Access Control ◽  
Online Social Networks ◽  
Online Social Network ◽  
Rapid Development ◽  
Real Life ◽  
Control Model ◽  
Mobile Internet ◽  
Access Control Model ◽  
Spatio Temporal

With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

scholarly journals Understanding attribute and social circle correlation in social networks

2019 ◽  
pp. 1228-1242
Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Coarse Grained ◽  
Unified Framework ◽  
Community Discovery ◽  
Social Circle ◽  
The Social ◽  
Circle Formation

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.

scholarly journals Finding a Weighted Positive In uence Dominating Set in E-learning Social Networks

2013 ◽  
Vol 10 (10) ◽  
pp. 2136-2145 ◽  
Author(s):  
Guangyuan Wang ◽  
Hua Wang ◽  
Xiaohui Tao ◽  
Ji Zhang ◽  
Guohun Zhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Community ◽  
Dominating Set ◽  
Online Social Network ◽  
Real Life ◽  
Synthetic Data ◽  
Positive Influence ◽  
Data Sets ◽  
E Learning

Online social network has developed significantly in recent years. Most of current research has utilized the property of online social network to spread information and ideas. Motivated by the applications of dominating set in social networks (such as e-learning), a variation of the dominating set called positive influence dominating set (PIDS) has been studied in the literature. The existing research for PIDS problem do not take into consideration the attributes, directions and degrees of personal influence. However, these factors are very important for selecting a better PIDS. For example, in a real-life e-learning community, the attributes and the degrees of their influence between a tutor and a student are different; the relationship between two e-learning users is asymmetrical. Hence, comprehensive, deep investigation of user’s properties become an emerging and urgent issue. The focus of this study is on the degree and direction between e-learners’ influence. A novel dominating set model called weighted positive influence dominating set (WPIDS), and two selection algorithms for the WPIDS problem have been proposed. Experiments using synthetic data sets demonstrate that the proposed model and algorithms are more reasonable and effective than those of the positive influence dominating set (PIDS) without considering the key factors of weight, direction and so on.

scholarly journals A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 221330-221351
Author(s):  
Munmun Bhattacharya ◽  
Sandip Roy ◽  
Kamlesh Mistry ◽  
Hubert P. H. Shum ◽  
Samiran Chattopadhyay
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Privacy Preserving ◽  
Network Applications ◽  
Location Sharing ◽  
Sharing Scheme

scholarly journals A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

Computers ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 42 ◽  
Author(s):  
Erfan Aghasian ◽  
Saurabh Garg ◽  
James Montgomery
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Online Social Network ◽  
Privacy Preserving ◽  
User Participation ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Sensitivity Score

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.

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