Differential Privacy for Edge Weights in Social Networks

Social networks can be analyzed to discover important social issues; however, it will cause privacy disclosure in the process. The edge weights play an important role in social graphs, which are associated with sensitive information (e.g., the price of commercial trade). In the paper, we propose the MB-CI (Merging Barrels and Consistency Inference) strategy to protect weighted social graphs. By viewing the edge-weight sequence as an unattributed histogram, differential privacy for edge weights can be implemented based on the histogram. Considering that some edges have the same weight in a social network, we merge the barrels with the same count into one group to reduce the noise required. Moreover, k-indistinguishability between groups is proposed to fulfill differential privacy not to be violated, because simple merging operation may disclose some information by the magnitude of noise itself. For keeping most of the shortest paths unchanged, we do consistency inference according to original order of the sequence as an important postprocessing step. Experimental results show that the proposed approach effectively improved the accuracy and utility of the released data

Information security awareness framework for enhancing security privacy among twitter users

Information security awareness is a concept that help to create a significant security policy aims to protect the privacy of Social networking site. Today social network users are connected to the internet service that assist all users to create user profile account. Moreover, Twitter can be recognized as one of the biggest and largest social network site. Twitter is one of the most significant social site that makes users to connect and make new friends around world. This research project is a platform that lies a crucial role to keep secure personal users’ information. Nowadays, user selfdisclosed is exposed in public on the social site without proper twitter privacy setting of the user account. In adding, this research project will help all the users of social networking site to be aware of all types of vulnerabilities behind the web base. Moreover, this project called privacy security awareness educate users to keep safe their twitter profile account. Accordingly, proposed twitter security privacy framework comprise four main sections components such as user security privacy, twitter privacy settings, security awareness for building relationship profile and user self-disclosed. Thus, this research project implied data analysis of qualitative survey of questionnaires which are distributed to the respondents. Besides, proposed information security awareness framework of twitter privacy among the students will provide a good security defense mechanism and at the same time used to rise a significant direction necessary to maintain a virtuous security policy among the social users. The analysis conducted has shown that most of the social networking site users are not aware of twitter privacy settings. Therefore, this research project conducted aimed to rise and protect the social networking site users from outsiders