Iron mask: Trust-preserving anonymity on the face of stigmatization in social networking sites

Abstract

Social networking sites are pervasively being used for seeking advice, asking questions, giving answers, and sharing experiences on various topics including health. When users share content about sensitive health topics, such as sexual dysfunction, infertility, or STDs, they may wish to do so anonymously to avoid stigmatization and the associated negative effects on mental health. However, a user masking their name with a pseudonym may still be inadvertently exposing their identity because of various quasi-identifiers present in their profile. One such quasi-identifier that has not been investigated in literature is the content itself, which could be used for authorship identification. Moreover, an anonymous user’s credibility cannot be established because their profile is no longer linked with their reputation. This study proposes the Iron Mask algorithm for providing enhanced anonymity while preserving trust. Iron Mask improves anonymity by using a probabilistic machine learning approach based on whiteprint identification and inclusion of content as a quasi-identifier. Iron Mask also introduces the concept of a trust-preserving pseudonym which masks user identity without loss of credibility. We evaluate the proposed algorithm using datasets from Quora, a question-answering social networking site, and demonstrate the efficacy of our algorithm with satisfactory recall and survey feedback results.