Impact of privacy invasion in social network sites

Data mining is a process of discovering unknown information from large datasets. Data mining is an effective way for a malicious hacker to extract information about people or any organization. This technique is useful when data from social networking sites like Facebook are taken into consideration. So, increasing use of social networking sites has lifted concerns about the misuse of people's’ privacy. The main aim of this study is to find how people's privacy is invaded using data mining technique. The proposed system applies Random decision forest algorithm to study the patterns of SNS users. It uses various attributes to make a decision forest for knowledge discovery. The Rule Id from decision forest is mainly used to predict the privacy of people. The result using random decision tree has shown the better performance and accuracy in finding the knowledge from the given Facebook information. The accuracy has increased by 70.836 to 78.854 %. The processing time is increased by 0.6 to 0.3 seconds. The proposed system has been able to find the sensitive patterns using the classifiers called random decision forest algorithm. By using different dataset, the result was more accurate and less time-consuming.