A Scalable and Privacy-Aware Location-Sensing Model for Ephemeral Social Network Service

Abstract

Social network services (SNSs) are developing at an explosive speed, which makes it easy for people to be closely connected. As a result, a new SNS type, ephemeral social network (ESN), is emerging to capture the ephemeral interactions and meetings that occur in environments such as conferences and workplaces. Most ESNs require the positioning for capturing the physical proximity between people, which impacts much the scalability, privacy protection, and the cost of the system. In this paper, we propose a scalable location-sensing model based on RFID-sensing architecture for ESN in consideration of four aspects of requirements, that is, the usability, QoS, scalability, and privacy. The model includes the perspectives of the privacy, architecture, deployment, and positioning algorithms, which can meet the four key requirements. A prototyping conference-type ESN system was also developed in this paper and was tested in the ACM UbiComp 2011 conference, which verified that the model works well with good scalability, low cost, and customized privacy protection.