Rapid advancement of the wireless technologies provide new opportunities for mobile users to have easy access to real-time data, derive useful social information, and stay connected with business partners, colleagues and friends. Towards this end, mobile social networking applications have recently emerged to meet these needs. Current mobile social networking applications do not support advanced context-based services. Additionally, serious security and privacy concerns have been raised when accessing social networking applications either from fixed locations or on-the-go. This project aims to build a secure mobile information sharing system (SEMOIS) that supports secure and privacy-preserving real-time information sharing. SEMOIS has the ability to store secure data items with flexible access control at insecure storage nodes and enables users to send context-based messages with late-binding features. SEMOIS achieves data confidentiality and privacy-preserving through data encryption and encrypted search, and enables intentional name based message dissemination without apriori knowledge of recipients. Additionally, a set of smart learning methods are developed to extract short-term and long-term geo-social patterns from multimodal sensing data collected by mobile devices for social networking purposes, e.g., geo-social patterns are used to derive hidden communities.
Project results are expected to advance the state of the art techniques for supporting secure and privacy-preserving mobile social networks with a variety of innovative features. The project equips both graduate and undergraduate students with the necessary background and practical skills for survival in the emerging job market and further contributes to the development of the pervasive computing field. In addition, SEMOIS can be used by middle and high school students from the Tri-State area that participate in the CHOICES and NSF-funded STEM program organized by Lehigh University.
