Minimizing User Vulnerability and Retaining Social Utility in Social Media

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breach with dire consequences. In our recent work, we show that it is feasible to measure user vulnerability and reduce one’s vulnerability without changing the structure of a social networking site. The approach is to unfriend one’s most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him would significantly reduce one’s own social status. In this work, we address the problem of vulnerability minimization with social utility constraints. We formally formulate the optimization problem, propose an approximation algorithm with a proven bound, and conduct empirical experiments with different forms of social utility on a large-scale Facebook dataset for performance evaluation and comparison.