Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are significantly concerned with the veracity of online dating profiles. To address some of these concerns, we present the user-centered design of an interface, called Certifeye, which aims to bootstrap trust in online dating profiles using existing social network data. Certifeye verifies that the information users report on their online dating profile (e.g., age, relationship status, and or photos) matches that displayed on their own Facebook profile. Finally, we present the results of a 161-user Mechanical Turk study assessing whether our veracity-enhancing interface successfully reduced concerns in online dating users and find a statistically significant trust increase.
New Web 2.0 applications, with their emphasis on collaboration and communication, hold the promise of major advances in social connectivity and coordination; however, they also increase the threats to user privacy. An important, yet under-researched privacy risk results from social inferences about user identity, location, and activities. In this paper, we frame the social inference problem. We then present the results from a 292 subject experiment that highlights: 1) the prevalence of social inference risks; 2) peoples difficulties in accurately predicting social inference risks; and 3) the relation between information entropy and social inference. We also show how to predict possible social inferences by modeling users background knowledge and calculating information entropy and discuss how social inference support systems can be deployed that protect user privacy.