AN PSYCHOANALYSIS OF DATA PRIVACY MAINTENANCE ISSUES IN SOCIAL NETWORK USING DATA MINING

2019 ◽  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Data Privacy ◽  
Using Data

scholarly journals A Psychoanalysis of Data Privacy Maintenance Issues in Social Network using Data mining

2019 ◽  
Vol 9 (1) ◽  
pp. 7018-7022
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Elliptic Curve Cryptography ◽  
Data Privacy ◽  
On Line ◽  
The Common ◽  
Using Data ◽  
Present Subject ◽  
Existing Data

The existing data sharing systems relates with the on-line social networks (OSNs) suggest encoding of information before sharing, the multiparty get to the executives of scrambled information has turned into a troublesome issue. A safe information sharing subject proposed in OSNs upheld figure content approach trait based and Elliptic Curve Cryptography algorithmic principle re-encryption and mystery sharing. The work relates the gatekeeper clients' delicate information grants clients to redo get to approaches of their information thus source scrambled information to the OSNs administration provider. The proposed technique displays a multiparty get to the executive’s model that enables the communicator to refresh the entrance strategy of figure content. The characteristics fulfill the common access strategy. The work needs a fractional mystery composing development inside which the calculation overhead of client is essentially diminished by strengthening the vast majority of the mystery composing activities to the OSNs administration provider. Moreover, the check capacity on the outcomes originated from the OSNs administration provider to guarantee the rightness of fractional decoded figure content. The present subject partner affordable properties disavowal philosophy that accomplishes each forward and in reverse mystery. The insurance and execution examination results demonstrate that the arranged subject is secure and efficient in OSNs.

Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 ◽  
Author(s):  
Taweesak Kuhamanee ◽  
Nattaphon Talmongkol ◽  
Krit Chaisuriyakul ◽  
Wimol San-Um ◽  
Noppadon Pongpisuttinun ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Using Data

Cognitive Social Mining Analysis Using Data Mining Techniques

2019 ◽  
pp. 99-117 ◽  
Author(s):  
Dharmpal Singh
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Computer Mediated ◽  
The Social ◽  
Using Data ◽  
Collaborative Activities

Social media are based on computer-mediated technologies that smooth the progress of the creation and distribution of information, thoughts, idea, career benefits and other forms of expression via implicit communities and networks. The social network analysis (SNA) has emerged with the increasing popularity of social networking services like Facebook, Twitter, etc. Therefore, information about group cohesion, contribution in activities, and associations among subjects can be obtained from the analysis of the blogs. The analysis of the blogs required well-known knowledge discovery tools to help the administrator to discover participant collaborative activities or patterns with inferences to improve the learning and sharing process. Therefore, the goal of this chapter is to provide the data mining tools for information retrieval, statistical modelling and machine learning to employ data pre-processing, data analysis, and data interpretation processes to support the use of social network analysis (SNA) to improve the collaborative activities for better performance.

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2014 ◽  
pp. 231-248
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

Performance Evaluation of Social Network Using Data Mining Techniques

2012 ◽  
pp. 25-49 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Ajith Abraham ◽  
Sachidananda Dehuri ◽  
Manas Ranjan Patra
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Social Network ◽  
Data Mining Techniques ◽  
Using Data

A Conceptual Framework for Social Network Data Security

2013 ◽  
pp. 58-86 ◽  
Author(s):  
Sanur Sharma ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
Community Analysis ◽  
Network Data ◽  
Data Mining Techniques ◽  
Social Network Data ◽  
The Social ◽  
Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

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