ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership.

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users' sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users' decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens' sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants' partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness.