CSR: Small: Collaborative Research: SEMOIS: Secure Mobile Information Sharing System

无线技术的快速发展为移动用户提供了新的机会，使他们能够轻松访问实时数据，获取有用的社交信息，并与商业伙伴、同事和朋友保持联系。为此，移动社交网络应用最近应运而生，以满足这些需求。当前的移动社交网络应用不支持高级的基于上下文的服务。此外，无论是从固定位置访问社交网络应用程序还是在移动中访问社交网络应用程序，都会引发严重的安全和隐私问题。该项目旨在建立一个安全的移动信息共享系统(Semois)，支持安全和隐私保护的实时信息共享。Semois具有在不安全的存储节点上通过灵活的访问控制存储安全数据项的能力，并使用户能够使用后期绑定功能发送基于上下文的消息。Semois通过数据加密和加密搜索来实现数据的机密性和隐私保护，并支持基于名称的有意消息传播，而无需接收者的先验知识。此外，还开发了一套智能学习方法，用于从移动设备收集的用于社交网络的多模式感知数据中提取短期和长期的地理社会模式，例如，使用地理社会模式来得出隐藏的社区。预计项目成果将推进最先进的技术，以支持具有各种创新功能的安全和隐私保护的移动社交网络。该项目为研究生和本科生提供了在新兴就业市场生存所需的背景和实用技能，并进一步促进了普及计算领域的发展。此外，参加由利哈伊大学组织的Choose和NSF资助的STEM项目的三州地区的初中生和高中生也可以使用Semois。

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.