Visible to the public Cloud-Assisted IoT Systems Privacy--2018Q3Conflict Detection Enabled

PI(s), Co-PI(s), Researchers: Fengjun Li, Bo Luo


The goal of this project is to develop principles and methods to model privacy needs, threats, and protection mechanisms in cloud-assisted IoT systems. The work aims to address the hard problems of resilient architectures, security metrics as well as scalability and composability.


Lei Yang, Sana Awan, Fengjun Li. Secure and Privacy-Preserving Classification for Smart IoT Applications. In Workshop on Securing the Internet of Things, KU SoS lablet, Kansas City, USA, October 2018 (Poster)


  • Extended the privacy-preserving classification protocol from the two-party model (i.e., client-server model) to a three-party model (i.e., client, server, and an assisting cloud).

We empirically tested the scalability of our three-party scheme to assess its effectiveness in IoT applications. We also extended the scheme from one-client case to multiple-client. The computation overload at each client in our scheme is independent of the number of classes but proportional to the number of features. The overall communication overhead is linear to the number of clients. These results are consistent with our asymptotic complexity analysis.


  • Fengjun Li, invited talk on Privacy-Preserving Classification for IoT Applications in HotPrivacy Day of IEEE Symposium on Privacy-Aware Computing, Washington D.C., September 26, 2018.
  • Fengjun Li, invited talk on Security and Privacy of Cloud-Assisted IoT Applications in Workshop on Securing the Internet of Things, KU SoS lablet, Kansas City, USA, October 2018.