Wireless and mobile communications, social technologies and sensors are connecting us, the Internet and the physical world into one piece, where tremendous amount of data are being collected on who we know, where we are, what we are doing and where we plan to go. Mining and analyzing these data help us better understand humans’ focus and even predict their future activities, thus facilitating us to develop better applications to serve people.

However, carelessly sharing the collected data for further data analysis incurs people’s intensive concern over the disclosure of their privacy. Without ensuring people’s privacy preservation, no one would like to use our services or technologies. However, preserving privacy is quite challenging. Essentially, privacy is the private information the owner of which is not willing to disclose to others, which is a context-based concept so that different people have different opinions about their privacy. For example, weight can be private information for Alice who indeed cares about her weight but not for Bob who does not care at all. The different understandings of privacy make it harder to propose a standard approach to preserving people’s privacy, which has attracted a lot of attention from researchers to make effort on the design of privacy preservation techniques. Nowadays, privacy preservation turns out to be really a hot topic. The researchers in our SPrinT group focus on handling privacy issues in challenging networks and systems, including Online Social Networks (OSNs), Wireless Sensor Networks (WSNs) as well as P2P healthcare data management systems.

Our work is centered around these key areas :

• Privacy Preservation in OSNs: In this project, we mainly cope with preserving users’ relationship privacy in publishing OSN data from the perspective of OSN site owners. We start by researching the relationship only between two users, and then extend our focus to preserving the relationships among a group of users on the OSNs.
• Data Source Location Preservation in WSNs: In this project, we intend to preserve the location privacy of data sources in wireless sensor networks, which use sensors to track a mobile target. On the one hand, we need to collect the target’s location information to monitor it; on the other hand, we have to preserve the location privacy of the sensors which detect the target, namely data sources, so that a malicious hunter is not able to conduct traffic analysis to locate the data sources aiming at catching the target.
• Privacy Preservation in Peer Data Integration System: The objective of this Project is to investigate the privacy data management issues in Peer-to-peer computing systems consists of a distributed computational peers, where each peer can exchange data and/or services with a set of other peers. This network is characterized by the lack of global control such as global resource management, or global schema and data repository.

Projects

• Pervasively Secure Infrastructures (PSI)
• Cloud Orchestrated Services for MObile Security (COSMOS)
• SHB: Collaborative Research: Crafting a Human-Centric Environment to Support Human Health Needs

People

Faculty

• Prof. Sajal K. Das
• Dr. Debraj De

Students

• Francesco Restuccia
• Mayank Raj

Collaborators

• Prof. Nan Zhang, George Washington University
• Prof. Bhavani Thuraisingham, UT Dallas
• Prof. Elisa Bertino, Purdue
• Prof. Latifur Khan, Collin County