Data Mining Techniques for Privacy Preservation in Social Network Sites Using SVM

2021 ◽  
pp. 733-743
Author(s):  
Vishvas Kalunge ◽  
S. Deepika
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Privacy Preservation ◽  
Social Network Sites ◽  
Data Mining Techniques

scholarly journals A Survey of Data Mining Techniques for Social Media Analysis

2014 ◽  
Vol 2014 ◽  
Author(s):  
Mariam Adedoyin-Olowe ◽  
Mohamed Medhat Gaber ◽  
Frederic Stahl
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Sites ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Media Analysis ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Wide Range

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors. Comment: 25 pages, 9 figures

Detecting Pharmaceutical Spam in Microblog Messages

2011 ◽  
pp. 101-115
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

scholarly journals Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data

2021 ◽  
Author(s):  
Rohit Ravindra Nikam ◽  
Rekha Shahapurkar
Keyword(s):  
Data Mining ◽  
Data Analytics ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Data Mining Techniques

Data mining is a technique that explores the necessary data is extracted from large data sets. Privacy protection of data mining is about hiding the sensitive information or identity of breach security or without losing data usability. Sensitive data contains confidential information about individuals, businesses, and governments who must not agree upon before sharing or publishing his privacy data. Conserving data mining privacy has become a critical research area. Various evaluation metrics such as performance in terms of time efficiency, data utility, and degree of complexity or resistance to data mining techniques are used to estimate the privacy preservation of data mining techniques. Social media and smart phones produce tons of data every minute. To decision making, the voluminous data produced from the different sources can be processed and analyzed. But data analytics are vulnerable to breaches of privacy. One of the data analytics frameworks is recommendation systems commonly used by e-commerce sites such as Amazon, Flip Kart to recommend items to customers based on their purchasing habits that lead to characterized. This paper presents various techniques of privacy conservation, such as data anonymization, data randomization, generalization, data permutation, etc. such techniques which existing researchers use. We also analyze the gap between various processes and privacy preservation methods and illustrate how to overcome such issues with new innovative methods. Finally, our research describes the outcome summary of the entire literature.

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
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