Secure and Privacy-Preserving Cyber-Physical Systems

RESUMEDans cette these de doctorat, nous etudions le probleme de conception d’estimateur et decommande preservant la confidentialite de donnees dans un systeme multi-algent composede systemes individuels lineaires incertains ainsi que le probleme de conception d’attaquesfurtives et d’estimateurs resilients aux attaques dans les systeme cyber-physiques. Les systemesde surveillance et de commande a grande echelle permettant une infrastructure deplus en plus intelligente s’appuient de plus en plus sur des donnees sensibles obtenues aupresd’agents prives. Par exemple, ces systemes collectent des donnees de localisation d’utilisateursd’un systeme de transport intelligent ou des donnees medicales de patients pour une detectionintelligente d’epidemie. Cependant, les considerations de confidentialite peuvent rendreles agents reticents a partager les informations necessaires pour ameliorer les performancesd’une infrastructure intelligente. Dans le but d’encourager la participation de ces agents, ils’avere important de concevoir des algorithmes qui traitent les donnees d’une maniere quipreserve leur confidentialite.Durant la premiere partie de cette these, nous considerons des scenarios dans lesquels lessystemes individuels sont independants et sont des systemes lineaires gaussiens. Nous revisitonsles problemes de filtrage de Kalman et de commande lineaire quadratique gaussienne(LQG), sous contraintes de preservation de la confidentialite. Nous aimerions garantir laconfidentialite differentielle, une definition formelle et a la pointe de la technologie concernantla confidentialite, et qui garantit que la sortie d’un algorithme ne soit pas trop sensibleaux donnees collectees aupres d’un seul agent. Nous proposons une architecture en deuxetapes, qui agrege et combine d’abord les signaux des agents individuels avant d’ajouter dubruit preservant la confidentialite et post-filtrer le resultat a publier. Nous montrons qu’uneamelioration significative des performances est offerte par cette architecture par rapport auxarchitectures standards de perturbations d’entree a mesure que le nombre de signaux d’entreeaugmente. Nous prouvons qu’un pre-filtre optimal d’agregation statique peut etre concu enresolvant un programme semi-defini. L’architecture en deux etapes, que nous developponsd’abord pour le filtrage de Kalman, est ensuite adaptee au probleme de commande LQGen exploitant le principe de separation. A travers des simulations numeriques, nous illustronsles ameliorations de performance de notre architecture par rapport aux algorithmes deconfidentialite differentielle qui n’utilisent pas d’agregation de signal.----------ABSTRACTThis thesis studies the problem of privacy-preserving estimator and control design in a multiagentsystem composed of uncertain individual linear systems and the problem of designof undetectable attacks and attack-resilient estimators for cyber-physical systems. Largescalemonitoring and control systems enabling a more intelligent infrastructure increasinglyrely on sensitive data obtained from private agents, e.g., location traces collected from theusers of an intelligent transportation system or medical records collected from patients forintelligent health monitoring. Nevertheless, privacy considerations can make agents reluctantto share the information necessary to improve the performance of an intelligent infrastructure.In order to encourage the participation of these agents, it becomes then critical to designalgorithms that process information in a privacy-preserving way. The first part of this thesisconsider scenarios in which the individual agent systems are linear Gaussian systems andare independent. We revisit the Kalman filtering and Linear Quadratic Gaussian (LQG)control problems, subject to privacy constraints. We aim to enforce differential privacy, aformal, state-of-the-art definition of privacy ensuring that the output of an algorithm is nottoo sensitive to the data collected from any single participating agent. We propose a twostagearchitecture, which first aggregates and combines the individual agent signals beforeadding privacy-preserving noise and post-filtering the result to be published. We show asignificant performance improvement offered by this architecture over input perturbationschemes as the number of input signals increases and that an optimal static aggregation stagecan be computed by solving a semidefinite program. The two-stage architecture, which wedevelop first for Kalman filtering, is then adapted to the LQG control problem by leveragingthe separation principle. We provide numerical simulations that illustrate the performanceimprovements over differentially private algorithms without first-stage signal aggregation.The second part of this thesis considers the problem of privacy-preserving estimator designfor a multi-agent system composed of individual linear time-invariant systems affected byuncertainties whose statistical properties are not available. Only bounds are given a priorifor these uncertainties. We propose a privacy-preserving interval estimator architecture,which releases publicly estimates of lower and upper bounds for an aggregate of the statesof the individual systems. Particularly, we add a bounded privacy-preserving noise to eachparticipant’s data before sending it to the estimator. The estimates published by the observerguarantee differential privacy for the agents’ data. We provide a numerical simulation thatillustrates the behavior of the proposed architecture.