Generating risk reduction recommendations to decrease vulnerability of public online profiles
Janet Zhu, Sicong Zhang, Lisa Singh, Grace Hui Yang, Micah Sherr · 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · 2016
Preserving online privacy is becoming increasingly challenging due in large part to the continued growth of social media. Those who choose to share their information publicly may not realize what features of their profiles make their public data more identifiable and potentially vulnerable to cross-site record linkage. This paper proposes a risk reduction recommendation method that suggests removal or modification of a small number of attributes to make a profile less unique, thereby reducing the identifiability and vulnerability of the user. Empirical results on data collected from Google+, LinkedIn, and Foursquare show that users' vulnerability in terms of identifiability and data exposure level can be significantly reduced while public profile utility can be maintained using our proposed approach.