Participants associated with the project:

Name Role Affiliation
Rick S. Blum Project Director Lehigh University
Visa Koivunen Co-Investigator Aalto University
H. Vincent Poor Co-Investigator Princeton University
H. Seyedmohammad Postdoctoral scholar Lehigh University
M. Hosseinitoushmanlouei Postdoctoral scholar Lehigh University
Y. Shkel Postdoctoral scholar Princeton University
H. Henri Graduate Student Aalto University
Ananth Samudrala Graduate Student Lehigh University
Topi Halme Graduate Student Aalto University
Martin Gölz Graduate Student Aalto University
Hassan Naseri Graduate Student Aalto University

Organizations Involved as Partners:

Name Type of Organization Location
Lehigh University Academic Institution Bethlehem, PA
Princeton University Academic Institution Princeton, NJ
Aalto University Academic Institution Finland



  • Zhang, Jiangfan and Blum, Rick S. and Poor, H. Vincent. (2018). Approaches to Secure Inference in the Internet of Things: Performance Bounds, Algorithms, and Effective Attacks on IoT Sensor Networks.  IEEE Signal Processing Magazine. 35 (5) 50 to 63.

  • Shkel, Yanina Y., Blum, Rick S., Poor, H. Vincent, Lossless compression and secrecy by design, submitted to IEEE Transactions on Information Theory, (2020).

  • Henri Hentilä, Visa Koivunen, Vincent Poor, and Rick Blum, Secure Key Generation for Distributed Inference in IoT, Conference on Information Science and Systems, 2019.

  • Samudrala, Ananth Narayan and Blum, Rick S.. (2018). On the estimation and secrecy capabilities of stochastic encryption for parameter estimation in IoT.  Conference on Information Sciences and Systems.  1 to 6. 

  • Samudrala, Ananth Narayan and Blum, Rick S.. (2017). Asymptotic analysis of a new low complexity encryption approach for the Internet of Things, smart cities and smart grid.  2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC).  200 to 204. 

  • Zhang, Jiangfan and Blum, Rick S. and Kaplan, Lance M. and Lu, Xuanxuan. (2018). A Fundamental Limitation on Maximum Parameter Dimension for Accurate Estimation With Quantized Data.  IEEE Transactions on Information Theory. 64 (9) 6180 to 6195. 

  • Perazzone, Jake and Graves, Eric and Yu, Paul and Blum, Rick. (2018). Inner Bound for the Capacity Region of Noisy Channels with an Authentication Requirement.  IEEE International Symposium on Information Theory.  126 to 130. 

  • Zhang, Jiangfan and Wang, Xiaodong and Blum, Rick S. and Kaplan, Lance M. (2018). Attack Detection in Sensor Network Target Localization Systems With Quantized Data.  IEEE Transactions on Signal Processing. 66 (8) 2070 to 2085. 

  • Karthik, Anantha K. and Blum, Rick S.. (2019). Optimum Full Information, Unlimited Complexity, Invariant, and Minimax Clock Skew and Offset Estimators for IEEE 1588.  IEEE Transactions on Communications. 67 (5) 3624 to 3637. 

  • Ramezani-Mayiami, Mahmoud and Hajimirsadeghi, Mohammad and Skretting, Karl and Blum, Rick S. and Vincent Poor, H.. (2019). Graph Topology Learning and Signal Recovery Via Bayesian Inference.  2019 IEEE Data Science Workshop (DSW).  52 to 56. 

  • Perazzone, Jake Bailey and Yu, Paul L. and Sadler, Brian M. and Blum, Rick S.. (2019). Physical Layer Authentication via Fingerprint Embedding: Min-Entropy Analysis.  Conference on Information Science and Systems.  

  • Perazzone, J. B and Yu, P. L. and Sadler, B. M and Blum, R. S.. (2019). Artificial noise and physical layer authentication: Miso regime.  IEEE Conference on Communications and Network Security.  1-5. 

  • K. G. Nagananda, R. S. Blum, and A. Koppel, ‘Reduced-rank least squares parameter estimation in the presence of byzantine sensors.’ Proc. IEEE CISS, Mar. 2020.

  • Karthik, Anantha K. and Blum, Rick S.. (2018). Improved Detection Performance for Passive Radars Exploiting Known Communication Signal Form.  IEEE Signal Processing Letters. 25 (11) 1625 to 1629.

Educational activities and Broader Impact

We trained several postdocs and students who we hope will take faculty positions at US universities.  We have taught them all the tasks they need to execute as faculty by working closely with them on papers, presentations, and instruction on how to teach and write proposals.

The research results have been incorporated into classes at Lehigh University, in summer school classes (IEEE, NSF, others), an IEEE distinguish lecture series (over 10 talks each year all over the world), and in lectures to engineers (local IEEE chapters) and the general public (Lehigh alumni and other interested parties). The research has been published in the best journals, conferences, and IEEE Signal Processing magazine (a top place to publish tutorial papers). Special sessions on the topics studied have been organized at top conferences every year the project has been running. These special sessions gather the top researchers to review the progress we have made and the progress they have made. These special sessions also highlight the work of our students and postdocs to help them obtain faculty positions.

The PIs (Blum and Poor) have both given plenary talks at major conferences, workshops, tutorials, and summer schools on our research. Prof. Blum gave talks each year on the cyber security work done under this project as an IEEE Signal Processing Society Distinguished Lecturer. We (our whole team) have organized outstanding special sessions at the Conference on Information Sciences and Systems (CISS) for each year the project has been running. Some years we organized several sessions.

In 2018, CISS was held in Princeton and our postdoc Yanina Shkel organized an invited session titled `”Theory and Bounds for IoT Security”. The session had talks from top researchers in information theoretic security on topics like physical layer security (e.g. the wiretap channel), secure short packet communication, network secrecy, privacy for large data sets, and many more.  The speakers were: Aylin Yener (Penn State) Matthieu Block (Georgia Tech) Aaron Wagner (Cornell University) Sennur Ulukus (University of Maryland) Lalitha Sankar (Arizona State University) Oliver Kosut (Arizona State University) and Yanina Shkel (Princeton).

Our collaborator from Finland, Visa Koivunen, also organized a session on inference processing for IoT at CISS 2018 that included our entire team. For example, our paper at this CISS session was by Ananth Narayan Samudrala, Rick S. Blum, H, V. Poor, and Visa Koivunen,  and was titled “On the Estimation and Secrecy Capabilities of Stochastic Encryption for Parameter Estimation in IoT”. The other papers in the session included:

Arpan Chattopadhyay and Urbashi Mitra (USC) “Dynamic Sensor Selection for Time-Varying Stochastic Process Tracking”

Natalia Vesselinova, Visa Koivunen, H. Vincent Poor (Our team), ” Large-Scale Nonparametric Distributed Inference using Bootstrap and FDR ”

Zhixiong Yang and Waheed U. Bajwa (Rutgers), “Distributed machine learning in the age of cyber attacks”

J. Heydari, S. Sihag, and A. Tajer (RPI), ”Quickest Search for Transient Changepoints under Composite Post-change Models”

In 2019, CISS was held at Johns Hopkins University. Prof. Blum organized a session at CISS 2019 with the following papers (including many from our team along with other outstanding researchers):

CISS Invited Session: Security and Inference for Internet of Things Networks

1. Lecture * INVITED *
Parameter Estimation and Secrecy by Design Yanina Shkel, Vincent Poor, Rick Blum

2. Lecture * INVITED Secure Key Generation for Distributed Inference in IoT Henri Hentilä, Visa Koivunen, Vincent Poor, Rick Blum

3. Lecture * INVITED * Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding: Security Analysis, Jake Perazzone, Paul Yu, Brian Sadler, Rick Blum

4. Lecture * INVITED * Optimal Sensor Placement for Topology Identification of Smart Power Grids Ananth Narayan Samudrala, Hadi Amini M, Soummya Kar, Rick Blum

5. Lecture * INVITED * Topology Attack Detection in Natural Gas Delivery Networks
Zisheng Wang, Rick Blum

In 2020, our collaborator Visa Koivunen organized another excellent session on Distributed Inference and Learning with the following papers:

Distributed Learning for Remote Estimation
Authors: Xu Zhang, Marcos Vasconcelos, Wei Cui and Urbashi Mitra

An Empirical Bayes Approach to Robust Mean Estimation with Application to Federated Learning
Authors: Jing Liu, Aditya Deshmukh, and Venugopal V. Veeravalli

Deterministic Multiple Change-Point Detection with Limited Communication
Authors: Eyal Nitzan, Topi Halme, H. Vincent Poor and Visa Koivunen

Cybersecurity of Inference in Vehicle Networks
Authors: Zisheng Wang and Rick S. Blum

Sequential Estimation of Network Cascades
Authors: Anirudh Sridhar and H. Vincent Poor

Distributed Joint Detection and Estimation: A Sequential Approach.
Authors: Dominik Reinhard, Michael Fauß and Abdelhak M Zoubir


We want to understand how much information can be transferred in secret for the resource 
constrained, short packet, low latency, inference applications most relevant to IoT.  Then we 
wanted to develop approaches that approach these rates.  This involves research in information 
theory, communications, signal processing, and inference.  We were also interesed in understanding 
the possible attacks on IoT inference systems, their impact and how to protect against them. We 
hoped to obtain fundamental theory, limits and protection approaches in the distributed processing 
environments typical in IoT.  Many important attacks on IoT and CPS systems, like electrical grids, 
vehicle networks and gas networks, can be described as changing the topology of a graph.  Due to 
this, we also hoped to perform fundamental research on detecting topology changes in graphs.  
Another, important topic is attacks on machine learning and reinforcement learning and we hoped to 
make contributions on that topic.   To augment research on attack detection and protection, it is also 
important to investigate authentication, especially at the physical layer where much remains uninvestigated.