Survey of analysis of user behavior in online social network

2016 ◽  
Author(s):  
Sawita Yousukkee
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Online Social Network

scholarly journals Trust-Based Access Control Model from Sociological Approach in Dynamic Online Social Network Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Seungsoo Baek ◽  
Seungjoo Kim
Keyword(s):  
Social Network ◽  
Access Control ◽  
User Behavior ◽  
Online Social Network ◽  
Sociological Approach ◽  
Access Control Models ◽  
Control Rules ◽  
Access Control Rules ◽  
Dynamic Trust ◽  
Privacy Breaches

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information.

scholarly journals A Community Finding Method for Weighted Dynamic Online Social Network Based on User Behavior

2015 ◽  
Vol 11 (6) ◽  
pp. 306160 ◽  
Author(s):  
Dongming Chen ◽  
Yanlin Dong ◽  
Xinyu Huang ◽  
Haiyan Chen ◽  
Dongqi Wang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Online Social Network ◽  
Community Finding ◽  
Finding Method

A new user behavior evaluation method in online social network

2019 ◽  
Vol 47 ◽  
pp. 217-222
Author(s):  
Min Yang ◽  
Shibin Zhang ◽  
Hang Zhang ◽  
Jinyue Xia
Keyword(s):  
Social Network ◽  
Evaluation Method ◽  
User Behavior ◽  
Online Social Network

scholarly journals Efficiently Verifiable Computations on User Data for Behavior Analysis with Privacy Preservation

2018 ◽  
Vol 7 (3.8) ◽  
pp. 87
Author(s):  
Miss. Pooja R. Kotwal ◽  
Prof. Mangesh M. Ghonge ◽  
Dr Amol D. Potgantwar
Keyword(s):  
Social Network ◽  
Behavior Analysis ◽  
Privacy Preservation ◽  
User Behavior ◽  
Online Social Network ◽  
Recent Decade ◽  
Third Party ◽  
Significant Information ◽  
Cryptographic Algorithm ◽  
User Data

Along with development of internet and web, online social network are becoming important information propagation platform with hundreds of million users worldwide. Online social network attract thousands of million users to use it every day for different purpose. So that tons of user behavior data is generated on internet. Developing endeavors have been committed to mining the inexhaustible behavior data to extract significant information for research purposes to  inquire about that, or analyst to develop better ecommerce strategies for business purpose. However the concern arises with this data is security, which is going to be presented to third parties. The most recent decade has seen an assortment of look into works endeavoring to perform information conglomeration in a privacy protecting manner. Most by far of existing  techniques give protection to users information yet at the cost of very limited data aggregation operations like calculating sum and mean of particular query, which barely fulfill the requirement of behavior analysis. So that, proposed system mainly focuses on privacy preservation and behavior analysis of online user data. In this paper we use general accumulation and specific collection for behavior analysis. Using cryptographic algorithm we prevent privacy disclosure from both  third party data aggregator and analyst.  We have executed our technique and assessed its execution utilizing a relational dataset. The results of the experiment shows that this research scheme handle both overall queries and various selective aggregate queries  with acceptable computation, privacy, and  overheads of the communication effectively.  

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