WiFiUS: Secure Inference in the Internet of Things

Principal Investigators:

  1. Prof. Rick S. Blum, Lehigh University.

  2. Prof. Vincent Poor, Princeton University.

  3. Prof. Visa Koivunen, Aalto University.

Goals of the Project:

  1. To understand how much information can be transferred in secret for the resource constrained, short packet, low latency, inference applications most relevant to IoT.

  2. Develop approaches that approach the theoretical rates.

  3. Understand the fundamental limits on one’s ability to compress data in IoT environments while still maintaining secrecy, along with compression and reconstruction algorithms to approach these bounds.

  4. Understand the possible attacks on IoT inference systems, their impact and how to protect against them.

  5. Understanding the limits of secrecy, privacy, and security for IoT inference applications employing statistical inference and machine learning in distributed settings.  How does distributed processing impact inference, security, secrecy, and authentication?   

  6. Develop distributed inference and learning methods for smart IoT sensors that fit with the developed secure communication approaches. Light encryption of short messages containing local inference results using common randomness.

  7. Low-complexity and energy efficient estimation, multiple hypothesis decision making and clustering methods for distributed smart sensors in IoT will be derived. Instead of explicitly assuming probability models, the empirical models will be learned from data to develop estimates or decision statistics and quantitative reliability measures.

  8. Novel fusion methods combining local inference results, performing multiple testing and unsupervised clustering and providing quantitative information about their reliability will be developed.

Project Details:

  1. Accomplishments

  2. Products

  3. Participants and Organizations Involved in the Project

  4. Impact of the Project

  5. Talks and Sessions

This work is supported by the National Science Foundation under grants CNS-1702555, and CNS-1702808 and by the Academy of Finland WiFiUS grant ”Secure Inference in the Internet of Things”.


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