 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.

ItemEnsuring privacy with constrained additive noise by minimizing Fisher informationFarokhi, F ; Sandberg, H (PERGAMONELSEVIER SCIENCE LTD, 20190101)The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a predefined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramér–Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology.

ItemSecurity Versus PrivacyFarokhi, F ; Esfahani, PM (IEEE, 20190118)Linear queries can be submitted to a server containing private data. The server provides response to the queries that are corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. A measure of privacy is defined which is inversely proportional to the trace of the Fisher information matrix. It is assumed that an adversary can inject a false bias to the responses. Thus, a measure of the security based on the KullbackLeiber divergence of the probability density functions of the response with and without the bias is defined. An optimization problem for balancing privacy and security is proposed and solved. It is shown that the level of guaranteed privacy times the level of security is always upper bounded by a constant. Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa.

ItemOptimal Stochastic Evasive Maneuvers Using the Schrodinger's EquationFarokhi, F ; Egerstedt, M (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20190701)In this letter, preys with stochastic evasion policies are considered. The stochasticity adds unpredictable changes to the prey's path for avoiding predator's attacks. The prey's cost function is composed of two terms balancing the unpredictability factor (by using stochasticity to make the task of forecasting its future positions by the predator difficult) and energy consumption (the least amount of energy required for performing a maneuver). The optimal probability density functions of the actions of the prey for tradingoff unpredictability and energy consumption is shown to be characterized by the stationary Schrödinger's equation.

ItemDevelopment and Analysis of Deterministic PrivacyPreserving Policies Using Non Stochastic Information TheoryFarokhi, F (IEEE, 201910)A deterministic privacy metric using nonstochastic information theory is developed. Particularly, maximin information is used to construct a measure of information leakage, which is inversely proportional to the measure of privacy. Anyone can submit a query to a trusted agent with access to a nonstochastic uncertain private dataset. Optimal deterministic privacypreserving policies for responding to the submitted query are computed by maximizing the measure of privacy subject to a constraint on the worstcase quality of the response (i.e., the worstcase difference between the response by the agent and the output of the query computed on the private dataset). The optimal privacypreserving policy is proved to be a piecewise constant function in the form of a quantization operator applied on the output of the submitted query. The measure of privacy is also used to analyze $k$ anonymity (a popular deterministic mechanism for privacypreserving release of datasets using suppression and generalization techniques), proving that it is in fact not privacy preserving.

ItemFisher information privacy with application to smart meter privacy using HVAC unitsFarokhi, F ; Sandberg, H ; Farokhi, F (Springer, 2020)In this chapter, we use Heating, Ventilation, and Air Conditioning (HVAC) units to preserve the privacy of households with smart meters in addition to regulating indoor temperature. We model the effect of the HVAC unit as an additive noise in the household consumption. The CramérRao bound is used to relate the inverse of the trace of the Fisher information matrix to the quality of an adversary’s estimation error of the household private consumption from the aggregate consumption of the household with the HVAC unit. This establishes the Fisher information as the measure of privacy leakage. We compute the optimal privacypreserving policy for controlling the HVAC unit through minimizing a weighted sum of the Fisher information and the cost operating the HVAC unit. The optimization problem also contains the constraints on the temperatures of the house.