Electrical and Electronic Engineering - Research Publications
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ItemPrivacy Against State Estimation: An Optimization Framework based on the Data Processing Inequality(ELSEVIER, 2020-01-01)Information about the system state is obtained through noisy sensor measurements. This data is coded and transmitted to a trusted user through an unsecured communication network. We aim at keeping the system state private; however, because the network is not secure, opponents might access sensor data, which can be used to estimate the state. To prevent this, before transmission, we randomize coded sensor data by passing it through a probabilistic mapping, and send the corrupted data to the trusted user. Making use of the data processing inequality, we cast the synthesis of the probabilistic mapping as a convex program where we minimize the mutual information (our privacy metric) between two estimators, one constructed using the randomized sensor data and the other using the actual undistorted sensor measurements, for a desired level of distortion–how different coded sensor measurements and distorted data are allowed to be.
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ItemOn Privacy of Dynamical Systems: An Optimal Probabilistic Mapping Approach(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021)We address the problem of maximizing privacy of stochastic dynamical systems whose state information is released through quantized sensor data. In particular, we consider the setting where information about the system state is obtained using noisy sensor measurements. This data is quantized and transmitted to a (possibly untrustworthy) remote station through a public/unsecured communication network. We aim at keeping (part of) the state of the system private; however, because the network (and/or the remote station) might be unsecure, adversaries might have access to sensor data, which can be used to estimate the system state. To prevent such adversaries from obtaining an accurate state estimate, before transmission, we randomize quantized sensor data using additive random vectors, and send the corrupted data to the remote station instead. We design the joint probability distribution of these additive vectors (over a time window) to minimize the mutual information (our privacy metric) between some linear function of the system state (a desired private output) and the randomized sensor data for a desired level of distortion-how different quantized sensor measurements and distorted data are allowed to be. We pose the problem of synthesising the joint probability distribution of the additive vectors as a convex program subject to linear constraints. Simulation experiments are presented to illustrate our privacy scheme.
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ItemRigid-Profile Input Scheduling Under Constrained Dynamics With a Water Network Application(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-11)The motivation for this work stems from the problem of scheduling requests for flow at supply points located throughout an automated network of open-water channels. The off-take flows are rigid-profile inputs to the system dynamics. In particular, the channel operator can only shift orders in time to satisfy constraints on the automatic response to changes in the load. This leads to a nonconvex semi-infinite programming problem, with a sum-separable cost that encodes the collective sensitivity of end-users to scheduling delays. The constraints encode the linear time-invariant continuous-time dynamics and limits on the state across a continuous scheduling horizon. Discretization is used to arrive at a more manageable approximation of the semi-infinite program. A method for parsimoniously refining the discretization is applied to ensure continuous-time feasibility for solutions of the approximate problem. It is then shown how to improve the cost without loss of feasibility. Supporting analysis is provided, along with simulation results for a realistic irrigation channel setup to illustrate the approach.
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ItemNo Preview AvailableStructured computation of optimal controls for constrained cascade systems(Taylor & Francis, 2020-01)Constrained finite-horizon linear-quadratic optimal control problems are studied within the context of discrete-time dynamics that arise from the series interconnection of subsystems. A structured algorithm is devised for computing the Newton-like steps of primal-dual interior-point methods for solving a particular re-formulation of the problem as a quadratic program. This algorithm has the following properties: (i) the computation cost scales linearly in the number of subsystems along the cascade; and (ii) the computations can be distributed across a linear processor network, with localised problem data dependencies between the processor nodes and low communication overhead. The computation cost of the approach, which is based on a fixed permutation of the primal and dual variables, scales cubically in the time horizon of the original optimal control problem. Limitations in these terms are explored as part of a numerical example. This example involves application of the main results to model data for the cascade dynamics of an automated irrigation channel in particular.
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ItemPrivate routing and ride-sharing using homomorphic encryption(Institution of Engineering and Technology (IET), 2020-02-07)A framework for private and secure communication and interaction between agents interacting in transportation services is developed. An agent, i.e. a user, can ask questions or submit queries regarding whether the other agents, i.e. drivers, use the desired road at specific times of the day in an encrypted fashion. The authors developed the framework using semi-homomorphic encryption (namely, the Paillier's encryption method) to enable the algebraic manipulation of plain data without the need for decryption using appropriate computations over the encrypted data. Strong privacy and security guarantees are proved for the agents. Subsequently, the semi-homomorphic encryption method is utilised to develop privacy-aware ride-sharing and routing algorithms without the need for disclosing the origin and destination of the user.
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ItemImplementing homomorphic encryption based secure feedback control(Elsevier BV, 2020-04)This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier’s homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast.
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ItemSecure and Private Implementation of Dynamic Controllers Using Semihomomorphic Encryption(Institute of Electrical and Electronics Engineers (IEEE), 2020-09)This article presents a secure and private implementation of linear time-invariant dynamic controllers using Paillier's encryption, a semihomomorphic encryption method. To avoid overflow or underflow within the encryption domain, the state of the controller is reset periodically. A control design approach is presented to ensure stability and optimize performance of the closed-loop system with encrypted controller.