Networked Systems Meet Geometric Approaches: Two Use Cases on Privacy Protection and Distributed UIOs
We develop a general geometric approach to address two important applications in the context of networked systems: i) output allocation for privacy protection, and ii) design of distributed unknown-input observers. The fil-rouge of the geometric approach stems from its inherent independence from coordinates and from the time-domain that can be continuous- and discrete-time without any methodological difference. For use-case i), we consider the properties of output redundant systems through the lenses of a geometric approach by formulating an output allocation synthesis problem, which involves “concealing” input information from a malicious eavesdropper having access to the system output, while still allowing for a legitimate user to reconstruct it. It is shown that the solvability of this problem requires the availability of a redundant set of outputs. This very problem is instrumental to unveiling the fundamental geometric properties of output-redundant systems, which form the basis for our subsequent constructions and results. As a direct application, we demonstrate how output allocation can be employed to effectively protect the information of input information from certain output eavesdroppers with guaranteed results. For use-case ii), we design distributed state observers exchanging information through a network in dynamic systems for which measurements are distributed across nodes and are influenced by unknown inputs via different distribution matrices. Our distributed state estimation scheme is made of a network of observers, each reconstructing the entire system state, despite having access only to local measurements per se insufficient for full state observation. Existing methods impose strict rank conditions on the input and output matrices at each node, and they are only applicable to continuous-time systems. To overcome this limitation, we propose a geometric approach for designing distributed unknown input observers applicable in both continuous-time and discrete-time domains. By leveraging the properties of the conditioned invariant subspace at each node, we reconstruct parts of the state unaffected by local unknown inputs and fuse these estimates through network communication.
Bio: Thomas Parisini received the Ph.D. degree in electronic engineering and computer science from the University of Genoa, Italy, in 1993. He was an associate professor with Politecnico di Milano, Italy. He currently holds the chair of industrial control and is the head of the Control and Power Research Group, Imperial College London. He also holds a Distinguished Professorship at Aalborg University, Denmark. Since 2001, he has been the Danieli Endowed Chair of automation engineering with the University of Trieste, Trieste, Italy, where from 2009 to 2012, he was the Deputy Rector. In 2023, he held a “Scholar-in-Residence” visiting position with Digital Futures-KTH, Stockholm, Sweden. He has authored or coauthored a research monograph in the Communication and Control Series, Springer Nature, and more than 400 research papers in archival journals, book chapters, and international conference proceedings.
Parisini was the recipient of the Knighthood of the Order of Merit of the Italian Republic for scientific achievements abroad awarded by the Italian President of the Republic in 2023. In 2018 he received the Honorary Doctorate from the University of Aalborg, Denmark and in 2024, the IEEE CSS Transition to Practice Award. Moreover, he was awarded the 2007 IEEE Distinguished Member Award and was co-recipient of the IFAC Best Application Paper Prize of the Journal of Process Control, Elsevier, for the three-year period 2011-2013 and of the 2004 Outstanding Paper Award of IEEE Transactions on Neural Networks. In 2016, he was awarded as Principal Investigator with Imperial of the H2020 European Union flagship Teaming Project KIOS Research and Innovation Centre of Excellence led by the University of Cyprus with an overall budget of over 40 million Euros.
He was the 2021-2022 President of the IEEE Control Systems Society, and he was the Editor-in-Chief of IEEE Transactions on Control Systems Technology (2009-2016). He was the Chair of the IEEE CSS Conference Editorial Board (2013-2019). Also, he was the associate editor of several journals including the IEEE Transactions on Automatic Control and the IEEE Transactions on Neural Networks. He is currently an Editor of Automatica and the Editor-in-Chief of the European Journal of Control. He was the Program Chair of the 2008 IEEE Conference on Decision and Control and General Co-Chair of the 2013 IEEE Conference on Decision and Control. He is a Fellow of IEEE and IFAC. He serves as the Chair of the Awards of the IEEE Control Systems Society and as a Member of IEEE TAB Periodicals Review and Advisory Committee.