Misty clouds - A layered cloud platform for online user anonymity in Social Internet of Things

Abstract Online services typically collect data that are explicitly provided by users and metadata that are implicitly inferred from users’ activity patterns. The proclaimed goal of metadata collection is to support the quality of service, thus enhancing the Social Internet of Things (SIoT). To most users, it is not obvious that metadata express people’s lives to a large degree. Traditionally, security has focused on protecting communication content rather than the metadata associated with it. This provides a thin layer of privacy protection. Thus, a demand exists for privacy-preserving technologies that prevent metadata collection and aggregation. This paper focuses on a service’s ability to observe communication metadata that can be exploited to learn users’ identities and behavior patterns. Thus, a novel system, Misty Clouds, is proposed as a platform for creating anonymous Internet connections to address both security and performance issues. The performance evaluation shows that the desired level of anonymity can be achieved with tolerable performance overheads. Through a comparison analysis, it is shown that the new algorithm outperforms an existing algorithm, Tor. Additionally, the features that can facilitate the growth of Misty Clouds into a holistic privacy-preserving platform are discussed. Furthermore, a user survey was conducted to study users’ perceptions and attitudes.