SRIM Scheme: An Impression-Management Scheme for Privacy-Aware Photo-Sharing Users

With the development of online social networks (OSNs) and modern smartphones, sharing photos with friends has become one of the most popular social activities. Since people usually prefer to give others a positive impression, impression management during photo sharing is becoming increasingly important. However, most of the existing privacy-aware solutions have two main drawbacks: ① Users must decide manually whether to share each photo with others or not, in order to build the desired impression; and ② users run a high risk of leaking sensitive relational information in group photos during photo sharing, such as their position as part of a couple, or their sexual identity. In this paper, we propose a social relation impression-management (SRIM) scheme to protect relational privacy and to automatically recommend an appropriate photo-sharing policy to users. To be more specific, we have designed a lightweight face-distance measurement that calculates the distances between users' faces within group photos by relying on photo metadata and face-detection results. These distances are then transformed into relations using proxemics. Furthermore, we propose a relation impression evaluation algorithm to evaluate and manage relational impressions. We developed a prototype and employed 21 volunteers to verify the functionalities of the SRIM scheme. The evaluation results show the effectiveness and efficiency of our proposed scheme.