Research Projects

Cyber-Physical System Security

Project: Privacy Modeling and Protection for Cloud-Assisted IoT Systems

New security and privacy concerns, such as unauthorized access, modification, data leakage, data linkage and reidentification, arise when data is transferred among interconnected devices or to the cloud. The proposed project will investigate the privacy threats in the cloud-assisted IoT systems, in which distributed IoT data are collected and analyzed for different types of IoT applications. The goal of the proposed research is to develop a privacy threat analysis framework to provide a systematic methodology for modeling privacy threats in the cloud-assisted IoT systems.

Related publications:

  • Lei Yang, Chris Seasholtz, Bo Luo, and Fengjun Li, "Hide Your Hackable Smart Home From Remote Attacks: An Extra Network-Level Safeguard," in European Symposium on Research in Computer Security (ESORICS), Barcelona, Spain, September 1-7, 2018
  • Lei Yang and Fengjun Li, “Cloud-Assisted Privacy-Preserving Classification for IoT Applications,” in IEEE Conference on Communications and Network Security (CNS), Beijing, China, June 1-5, 2018. (acceptance rate 28%)
  • Abdulmali Humayed, Jinqiang Lin, Fengjun Li, and Bo Luo, “Cyber-Physical Systems Security -- A Survey,” in IEEE Internet of Things Journal - Special Issue on Security and Privacy in Cyber-Physical Systems, PP(99):1-1, 2017.
  • Lei Yang, Humayed Abudulmalik, and Fengjun Li, "A Multi-Cloud based Privacy-Preserving Data Publishing Scheme for the Internet of Things," in Annual Computer Security Applications Conference (ACSAC), Los Angeles, CA, December 2016.

Project: Smart Grid Security

In this project, we developed one of the first solutions to address both security and privacy concerns in the collection of fine-grained real-time smart metering data in smart grid neighborhood area networks (NANs) using hommomorphic encryption algorithms. After that, we developed a set of algorithms for efficient privacy-preserving in-network data operations, integrity verification, and detection of false data injection.

Related publications:

Social Network Security and Privacy

Project: Social Network Spam Detection

Customers' reviews would greatly affect others’ purchase decision making. It becomes an increasing incentive for review spammers to manipulate the reviews. Thus, the detection of review spams and a trustworthy measure of the credability of the review are critical to online review sites and the online retailers. In this research thrust, we aim to create a robust spam measurement and detection model that incorporates both content and structure features.

Related publications:

  • Hao Xue and Fengjun Li, "Online Review Spam Detection through Content-Aware Trust Propagation," in the 31st Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec), Philadelphia, PA, July 2017.
  • Hyunjin Seo, James Sterbenz, Fengjun Li and Shiva Velma, "Multilevel Analysis of Networked Movements in Digital Age," in International Communication Association Conference, 2017.
  • Pegah Nokhiz and Fengjun Li, "Understanding Rating Behavior based on Moral Foundations: The case of Yelp Reviews," in IEEE BigData Workshop on Big Data Technology and Ethics Considerations in Customer Behavior and Customer Feedback Mining, Boston, MA, 2017.
  • Hyunjin Seo, Fengjun Li, Roseann Pluretti, Hao Xue, and Shiva Velma, "Perceptions of Online Reviews: Motivation, Sidedness, and Reviewer Information," in Journalism and Mass Communication Annual Conference, Minneapolis, MN, August 2016.
  • Hao Xue, Fengjun Li, Hyunjin Seo, and Roseann Pluretti, "Trust-Aware Review Spam Detection," in the 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Symposium on Recent Advances of Trust, Security and Privacy in Computing and Communications, Helsinki, Finland, Aug 2015.
  • Hyunjin Seo, Fengjun Li, Jeongsub Lim, Roseann Pluretti, Hao Xue, and Sreenivas Kumar Vekapu, "User ratings of yelp reviews: A big data analysis approach," in Association for Education in Journalism and Mass Communication Annual Conference, San Francisco, CA, August 2015.
  • Hyunjin Seo, Fengjun Li, Roseann Pluretti, Hao Xue, and Sreenivas Kumar Vekapu, "Perceived usefulness of online reviews: Effects of review characteristics and reviewer attributes," in Proceedings of the 65th International Communication Association Annual Conference, San Juan, Puerto Rico, May 2015.
  • Yingying Ma and Fengjun Li, "Detecting Review Spam: Challenges and Opportunities," in the Collaborative Communities for Social Computing (CCSocialComp) Workshop, Pittsburgh, PA, October 2012. (invited)

Project: Social Network Privacy

Privacy is becomg an increasing concern in applications related to online social networks. In these "uncontrolled" environments, individuals create, share and propagate information voluntarily. The objective of the privacy studies in OSN is to find answers to essential questions as “how identifiable an individual is from scattered information pieces over social media” and “how to protect the privacy of personal information from being unknowingly revealed”.

Related publications:

Network Security

Project: Internet Anonymity

Anonymous rotuing enables people to hide their identity in communication over the Internet. Most of the existing anonymous routing protocols rely on a relative small set of pre-selected relay servers to redirect the messages. The pre-selection approaches provide good anonymity, but suffer from node failures and scalability problem. In this project, we develope a node-failure-resilient anonymous, CAT (Communtative Anonymous Tunnel), to allow the initiator to explore an anonymous tunnel consisting of several valid anonymous paths via a probing process, and hop among them when the active path encounters a node failure.

Related publications:

  • Lei Yang and Fengjun Li, "Enhancing Traffic Analysis Resistance for Tor Hidden Services with Multipath Routing," in the 11th EAI International Conference on Security and Privacy in Communication Networks (SecureComm), Dallas, USA, October, 2015. (Best Paper Award Winner)
  • Lei Yang and Fengjun Li, "mTor: A Multipath Tor Routing beyond Bandwidth Throttling," in IEEE Conference on Communications and Network Security (CNS), Florence, Italy, September 2015.
  • Lei Yang and Fengjun Li, "Enhancing Traffic Analysis Resistance for Tor Hidden Services with Multipath Routing," In IEEE Conference on Communications and Network Security (CNS), Florence, Italy, September 2015. (Poster)
  • Fengjun Li, Bo Luo, Peng Liu, and Chao-Hsien Chu, "A Node-failure-resilient Anonymous Communication Protocol through Commutative Path Hopping," in Proceedings of the 29th IEEE Conference on Computer Communications (INFOCOM), San Diego, CA, March 2010. (acceptance rate: 17%)

Privacy-preserving Information Sharing

I have been working on privacy-enhancing federated information sharing and proposed a secure multi-organizational information sharing framework to support distributed privacy-preserving data access in business intelligence systems and health information exchange systems. This research aims to support privacy-enhancing federated information sharing across multiple organizations with various information sharing needs and requirements upon different levels of trust. We have proposed a secure multi-organizational information sharing framework to support distributed privacy-preserving data access in business intelligence systems and health information exchange systems. This Privacy-Preserving Information Brokering (PPIB) framework combines XML access control enforcement mechanism with content-based inquiry routing across databases belonging to multiple alliance organizations, and seamlessly integrates both functionalities into a set of nondeterministic finite automata (known as information brokers).

Related publications: