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.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Predicting paper making defects on-line using data mining

1998 ◽  
Vol 11 (5-6) ◽  
pp. 331-338 ◽  
Author(s):  
Robert Milne ◽  
Mike Drummond ◽  
Patrick Renoux
Keyword(s):  
Data Mining ◽  
Paper Making ◽  
On Line ◽  
Using Data

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

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.

Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

scholarly journals Private algorithms for the protected in social network search

2016 ◽  
Vol 113 (4) ◽  
pp. 913-918 ◽  
Author(s):  
Michael Kearns ◽  
Aaron Roth ◽  
Zhiwei Steven Wu ◽  
Grigory Yaroslavtsev
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Data Privacy ◽  
Large Scale ◽  
Graph Search ◽  
Medical Testing ◽  
Background Checks ◽  
Network Search ◽  
Natural Variants

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

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