What was accomplished under these goals?
- We have been developing fundamental theory showing how much information can be securely transferred while also achieving compression. The theory is lacking in this area. There is a need to extend the theory of compression to cases where security is desired. Is it possible that significant compression can be achieved while maintaining secure communication?
- We have are also studying the theory of security for IoT inference applications. We want to understand the types of possible attacks and how to protect against them.
Significant Results:
- We have made significant progress in developing some fundamental theory showing how much information can be securely transferred while also achieving compression. We have very important new results that build the theory in this area. The results show how to extend the theory of compression to cases where security is desired. The results show that if we secure only certain parts of the data, which is a very practical assumption, significant compression can still be achieved.
- We have also made significant progress on the theory of security for IoT inference applications. We have classified all possible attacks, those of greatest impact and we have shown how to protect against these attacks.
- We have developed theory and methods for distributed decision making and fusion approaches where underlying probability models are learned and approximated by empirical distributions.
- We have developed spatial multiple hypotheses testing methods and fusion methods for performing inferences about the state of spatially varying fields and unsupervised learning through clustering. We have analyzed their performance using both analytical methods and simulations.
- We have developed methods for lightweight encryption and secure short packet communication of local inference results to the fusion center. Key generation is based on common randomness between each sensor and the fusion center.
What opportunities for training and professional development has the project provided?
We are training several postdocs and students who we expect will take faculty positions at US universities. We are teaching the students and postdocs how to be successful in these positions.
How have the results been disseminated to communities of interest?
We have published our results in the top journals and conferences and presented our work at the top universities in the world. The PIs (Blum and Poor) have both given plenary talks at major conferences, workshops, tutorials, and summer schools on our research. We have organized special sessions at the Conference on Information Sciences and Systems (CISS) for each of the two years the project has been running. Last year CISS was held at Princeton University. This year we have another session at CISS at John Hopkins University.
Papers presented at CISS-2019 Session on Security and Inference on IoT Networks:
-
Y. Shkel and H. V. Poor, “Parameter Estimation and Secrecy by Design”.
-
H. Hentila, V. Koivunen, H. V. Poor, and R. S. Blum, “Secure Key Generation for Distributed Inference in IoT”.
-
J. Perazzone, P. Yu, B. Sadler, and R. S. Blum, “Cryptographic Side Channel Signaling and Authentication via Fingerprint Embedding: Security Analysis”.
-
A. Samudrala, H. Amini, S. Kar, and R. S. Blum, “Optimal Sensor Placement for Topology Identification of Smart Power Grids”.
-
Z. Wang and R. S. Blum, “Topology Attack Detection in Natural Gas Delivery Networks”.
Papers presented at CISS-2018 Session on Inference processing for IoT:
-
A. Samudrala, R. S. Blum, H. V. Poor, and Visa Koivunen, “On the Estimation and Secrecy Capabilities of Stochastic Encryption for Parameter Estimation in IoT”, CISS-2018.
-
A. Chattopadhyay and U. Mitra, “Dynamic Sensor Selection for Time-Varying Stochastic Process Tracking”.
-
Z. Yang and W. U. Bahwa, “Distributed Machine Learning in the age of cyber attacks”.
-
J. Heydari, S, Sihag and A. Tajer, “Quickest search for Transient Changepoints under Composite Post-change Models”.
Papers presented at CISS-2018 Session on Theory and Bounds for IoT Security:
-
O.Kosut and J. Kliewer, “Finite Blocklength bounds for arbitrarily varying channel”.
-
C. Huang, P. Kairouz, X. Chen, L. Sankar, and R. Rajagopal, “Generative Adversarial Privacy: A Data-Driven Approach for Guaranteeing Privacy and Utility”.
-
Y. Wei, K. Banawan and S. Ulukus, “Private information with partially known private side information”.
-
I. Issa and A. Wagner, “Learning Maximal Leakage”.
-
Y. Shkel, R. S. Blum and H. V. Poor, “Secure Lossless Compression”.
-
A. Zewail and A. Yener, “Cache-aided combination networks with Secrecy Guarantees”.