Research, so far, has shown that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable providing users' interaction data. To address these privacy concerns, users on a social networking site like Faceboook are usually left with profile settings to mark some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a "Faceboook page"). Although the aim of such interactions is to help users to become more social, visibilities of these interactions are beyond their profile settings and publicly accessible to everyone. The focus of this paper is to explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, we ask - are users' personal attributes predictable using only the unprotected interactions? To answer this question, we design a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge for the proposed problem. Therefore, we first provide a way to mitigate the data sparsity challenge and propose a novel attribute prediction framework using only the unprotected interactions. Experimental results on Faceboook dataset demonstrates that the proposed framework can predict users' personal attributes.