International Journal of Computational Research and Development (IJCRD) Impact Factor: 4.775, ISSN (Online): 2456 - 3137 (www.dvpublication.com) Volume 1, Issue 2, 2016 107 DEDUCTION ATTACK ON BROWSING HISTORY IN TWITTER USING PUBLIC CLICK ANALYTIC AND METADATA K. Dharani*, M. Rajesh** & Dr. T. Senthil Prakash*** * PG Scholar, Department of Computer Science and Engineering, Shree Venkateshwara Hi-Tech Engineering College, Gobi, Tamilnadu ** Assistant Professor, Department of Computer Science and Engineering, Shree Venkateshwara Hi-Tech Engineering College, Gobi, Tamilnadu *** Head, Department of Computer Science and Engineering, Shree Venkateshwara Hi-Tech Engineering College, Gobi, Tamilnadu Cite This Article: K. Dharani, M. Rajesh & Dr. T. Senthil Prakash, “Deduction Attack on Browsing History in Twitter Using Public Click Analytic and Metadata”, International Journal of Computational Research and Development, Volume 1, Issue 2, Page Number 107-110, 2016. Abstract: Our attacks rely on the combination of publicly available information: click analytics from URL shortening services and metadata from Twitter. The goal of the attacks is to know which URLs are clicked on by target users. We introduce two different attack methods: (i) an attack to know who click on the URLs updated by target users and (ii) an attack to know which URLs are clicked on by target users. To perform the first attack, we find a number of Twitter users who frequently distribute shortened URLs, and investigate the click analytics of the distributed shortened URLs and the metadata of the followers of the Twitter users. To perform the second attack, we create monitoring accounts that monitor messages. This work represents inference attack on browsing information in public click analytic in twitter metadata from all followings of target users to collect all shortened URLs that the target users may click on. We then monitor the click analytics of those shortened URLs and compare them with the metadata of the target user. Key Words: URL Shortening Services, Twitter, Public Click Analytic & Metadata Introduction: Overall Description: We proposed system attack methods for inferring whether a specific user clicked on certain shortened URLs on Twitter. Our attacks rely on the combination of publicly available information: click analytics from URL shortening services and metadata from Twitter. Two different attack methods: (i) an attack to know who click on the URLs updated by target users and (ii) an attack to know which URLs are clicked on by target users. To perform the first attack, we find a number of Twitter users who frequently distribute shortened URLs, and investigate the click analytics of the distributed shortened URLs and the metadata of the followers of the Twitter users. To perform the second attack, we create monitoring accounts that monitor messages from all followings of target users to collect all shortened URLs that the target users may click on. Then monitor the click analytics of those shortened URLs and compare them with the metadata of the target user. System Architecture: Problem Definition: There are several types of history stealing attacks. First, attackers exploit cascading style sheet visited styles. They use the fact that browsers display visited links differently from unvisited links. They analyze behaviors of each browser related to CSS visited styles and build a system to detect browsing history of users efficiently. Second, attackers exploit browser and DNS cache to conduct history stealing attacks. Felton and Schneider describe attack methods using browser and DNS cache. Third, some researchers propose attack methods to steal browsing history using user interactions and side-channels. They also use a webcam to detect User Using shortened url Message post Public Click Analytic Inference attacker identify attackers Identify user URL Prevent user privacy in URL attackers brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by ZENODO