 Electrical and Electronic Engineering  Research Publications
Electrical and Electronic Engineering  Research Publications
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ItemOn Privacy of Quantized Sensor Measurements through Additive NoiseMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D ( 20180910)We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion  how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.

ItemInformationTheoretic Privacy through Chaos Synchronization and Optimal Additive NoiseMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D ( 20190603)We study the problem of maximizing privacy of data sets by adding random vectors generated via synchronized chaotic oscillators. In particular, we consider the setup where information about data sets, queries, is sent through public (unsecured) communication channels to a remote station. To hide private features (specific entries) within the data set, we corrupt the response to queries by adding random vectors. We send the distorted query (the sum of the requested query and the random vector) through the public channel. The distribution of the additive random vector is designed to minimize the mutual information (our privacy metric) between private entries of the data set and the distorted query. We cast the synthesis of this distribution as a convex program in the probabilities of the additive random vector. Once we have the optimal distribution, we propose an algorithm to generate pseudorandom realizations from this distribution using trajectories of a chaotic oscillator. At the other end of the channel, we have a second chaotic oscillator, which we use to generate realizations from the same distribution. Note that if we obtain the same realizations on both sides of the channel, we can simply subtract the realization from the distorted query to recover the requested query. To generate equal realizations, we need the two chaotic oscillators to be synchronized, i.e., we need them to generate exactly the same trajectories on both sides of the channel synchronously in time. We force the two chaotic oscillators into exponential synchronization using a driving signal. Exponential synchronization implies that trajectories of the oscillators converge to each other exponentially fast for all admissible initial conditions and are perfectly synchronized in the limit only. Thus, in finite time, there is always a “small” difference between their trajectories. To implement our algorithm, we assume (as it is often done in related work) that systems have been operating for sufficiently long time so that this small difference is negligible and oscillators are practically synchronized. We quantify the worstcase distortion induced by assuming perfect synchronization, and show that this distortion vanishes exponentially fast. Simulations are presented to illustrate our results.

ItemOn Privacy of Quantized Sensor Measurements through Additive NoiseMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D (IEEE, 20180101)We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion  how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.

ItemInformationtheoretic privacy through chaos synchronization and optimal additive noiseMurguia, C ; Shames, I ; Farokhi, F ; Nešić, D ; Farokhi, F (Springer, 2020)We study the problem of maximizing privacy of data sets by adding random vectors generated via synchronized chaotics oscillators. In particular, we consider the setup where information about data sets, queries, is sent through public (unsecured) communication channels to a remote station. To hide private features (specific entries) within the data set, we corrupt the response to queries by adding random vectors.We send the distorted query (the sum of the requested query and the random vector) through the public channel. The distribution of the additive random vector is designed to minimize the mutual information (our privacy metric) between private entries of the data set and the distorted query. We cast the synthesis of this distribution as a convex program in the probabilities of the additive random vector. Once we have the optimal distribution, we propose an algorithm to generate pseudorandom realizations from this distribution using trajectories of a chaotic oscillator. At the other end of the channel, we have a second chaotic oscillator, which we use to generate realizations from the same distribution. Note that if we obtain the same realizations on both sides of the channel, we can simply subtract the realization from the distorted query to recover the requested query. To generate equal realizations, we need the two chaotic oscillators to be synchronized, i.e., we need them to generate exactly the same trajectories on both sides of the channel synchronously in time. We force the two chaotic oscillators into exponential synchronization using a driving signal. Simulations are presented to illustrate our results.

ItemOptimal contract design for effortaverse sensorsFarokhi, F ; Shames, I ; Cantoni, M (Taylor & Francis, 20180628)A central planner wishes to engage a collection of sensors to measure a quantity. Each sensor seeks to tradeoff the effort it invests to obtain and report a measurement, against contracted reward. Assuming that measurement quality improves as a sensor increases the effort it invests, the problem of reward contract design is investigated. To this end, a game is formulated between the central planner and the sensors. Using this game, it is established that the central planner can enhance the quality of the estimate by rewarding each sensor based on the distance between the average of the received measurements and the measurement provided by the sensor. Optimal contracts are designed from the perspective of the budget required to achieve a specified level of error performance.

ItemSecure Networked Control Systems Design Using Semihomomorphic EncryptionLin, Y ; Farokhi, F ; Shames, I ; Nešić, D ; Ferrari, RMG ; Teixeira, AMH (Springer, 20210101)A secure and private nonlinear networked control systems (NCSs) design using semihomomorphic encryption is studied. Static feedback controllers are used and network architectures are provided to enable control signal computation using encrypted signals directly. As a result, the security of the NCSs is further enhanced by preserving the privacy of information flowing through the whole network. Whereas in traditional encryption techniques, encrypted signals are decrypted before control computation and are encrypted again after computation for transmission. While this is highly desirable from privacy point of view, additional technical difficulties in the design and analysis of NCSs are induced compared to standard NCSs. In this chapter, we provide sufficient conditions on the encryption parameters that guarantee robust stability of the NCS in the presence of disturbances in a semiglobal practical sense and discuss the tradeoffs between the required computational resources, security guarantees, and the closedloop performance. The proof technique is based on Lyapunov methods.

ItemPrivacy Against State Estimation: An Optimization Framework based on the Data Processing InequalityMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D (ELSEVIER, 20200101)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.

ItemOn Privacy of Dynamical Systems: An Optimal Probabilistic Mapping ApproachMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D ; Poor, HV (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20210101)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 distortionhow 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.

ItemRigidProfile Input Scheduling Under Constrained Dynamics With a Water Network ApplicationLang, A ; Cantoni, M ; Farokhi, F ; Shames, I (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20211101)The motivation for this work stems from the problem of scheduling requests for flow at supply points located throughout an automated network of openwater channels. The offtake flows are rigidprofile 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 semiinfinite programming problem, with a sumseparable cost that encodes the collective sensitivity of endusers to scheduling delays. The constraints encode the linear timeinvariant continuoustime dynamics and limits on the state across a continuous scheduling horizon. Discretization is used to arrive at a more manageable approximation of the semiinfinite program. A method for parsimoniously refining the discretization is applied to ensure continuoustime 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.

ItemNo Preview AvailableStructured computation of optimal controls for constrained cascade systemsCantoni, M ; Farokhi, F ; Kerrigan, E ; Shames, I (Taylor & Francis, 202001)Constrained finitehorizon linearquadratic optimal control problems are studied within the context of discretetime dynamics that arise from the series interconnection of subsystems. A structured algorithm is devised for computing the Newtonlike steps of primaldual interiorpoint methods for solving a particular reformulation 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.