We investigate the problem of sharing the outcomes of a parametric source with an untrusted party while ensuring the privacy of the parameters. We propose privacy mechanisms which guarantee parameter privacy under both Bayesian statistical as well as information-theoretic privacy measures. The properties of the proposed mechanisms are investigated and the utility-privacy trade-off is analyzed.

This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bi-level optimization model is developed, which computes the optimal allocation of new wind and battery capacities, by minimizing a weighted sum of the average market price and price volatility. A fixed budget is allocated on wind and battery capacities in the upper-level problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lower-level problem using a stochastic (Bayesian) Cournot-based game model. Australia's national electricity market, which is experiencing occasional price peaks, is considered as the case study. Our simulation results quantitatively illustrate that the regulated wind is more efficient than storage in reducing the average price, while the regulated storage more effectively reduces the price volatility. According to our numerical results, the storage-only solution reduces the average price at most by 9.4%, and the wind-only solution reduces the square root of price volatility at most by 39.3%. However, an optimal mixture of wind and storage can reduce the mean price by 17.6% and the square root of price volatility by 48.1%. It also increases the consumer surplus by 1.52%. Moreover, the optimal mixture of wind and storage is a profitable solution unlike the storage-only solution.

This paper studies the sensor placement problem in a networked control system for improving its security against cyber-physical attacks. The problem is formulated as a zero-sum game between an attacker and a detector. The attacker's decision is to select f nodes of the network to attack whereas the detector's decision is to place f sensors to detect the presence of the attack signals. In our formulation, the attacker minimizes its visibility, defined as the system L2 gain from the attack signals to the deployed sensors' outputs, and the detector maximizes the visibility of the attack signals. The equilibrium strategy of the game determines the optimal locations of the sensors. The existence of Nash equilibrium for the attacker-detector game is studied when the underlying connectivity graph is a directed or an undirected tree. When the game does not admit a Nash equilibrium, it is shown that the Stackelberg equilibrium of the game, with the detector as the game leader, can be computed efficiently. Our results show that, under the optimal sensor placement strategy, an undirected topology provides a higher security level for a networked control system compared with its corresponding directed topology.

We propose a game-theoretic framework for improving the resilience of multi-agent consensus dynamics in the presence of a strategic attacker. In this game, the attacker selects a set of network nodes to inject the attack signals. The attacker's objective is to minimize the required energy for steering the consensus towards its desired direction. This energy is captured by the trace of controllability Gramian of the system when the input is the attack signal. The defender improves the resilience of dynamics by adding self-feedback loops to certain nodes of the system and its objective is to maximize the trace of controllability Gramian. The Stackelberg equilibrium of the game is studied with the defender as the game leader. When the underlying network topology is a tree and the defender can select only one node, we show that the optimal strategy of the defender is determined by a specific distance-based network centrality measure, called network's f-center. In addition, we show that the degree-based centralities solutions may lead to undesirable payoffs for the defender. At the end, we discuss the case of multiple attack and defense nodes on general graphs.

Network control systems (NCSs) heavily rely on information and communication technologies for sharing information between sensors and controllers as well as controllers and actuators. When estimation, control or actuation tasks in a NCS are performed by an untrusted party, sharing information might result in the leakage of private information. The current paper reviews some of the recent results on the privacy-aware decision-making problems in NCSs. In particular, we focus on static and dynamic decision-making problems wherein privacy is measured using information-theoretic notions. We also review the applications of these problems in smart buildings and smart grids. 

A malicious attacker with access to the sensor channel in a feedback control system can severely affect the physical system under control, while simultaneously being hard to detect. A properly designed anomaly detector can restrict the impact of such attacks, however. Anomaly detectors with an internal state (stateful detectors) have gained popularity because they seem to be able to mitigate these attacks more than detectors without a state (stateless detectors). In the analysis of attacks against control systems with anomaly detectors, it has been assumed that the attacker has access to the detector's internal state, or designs its attack such that it is not detected regardless of the detector's state. In this paper, we show how an attacker can realize the first case by breaking the confidentiality of a stateful detector state evolving with linear dynamics, while remaining undetected and imitating the statistics of the detector under nominal conditions. The realization of the attack is posed in a convex optimization framework using the notion of Kullback-Leibler divergence. Further, the attack is designed such that the maximum mean estimation error of the Kalman filter is maximized at each time step by exploiting dual norms. A numerical example is given to illustrate the results.

This paper studies an optimal decision fusion problem with a group of human decision makers when an order effect is present. The order effect refers to situations wherein the process of decision making by a human is affected by the order of decisions. In our set-up, all human decision makers, called observers, receive the same data which is generated by a common but unknown hypothesis. Then, each observer independently generates a sequence of decisions which are modeled by employing non-commutative probabilistic models of the data and their relation to the unknown hypothesis. The use of non-commutative probability models is motivated by recent psychological studies which indicate that these non-commutative probability models are more suitable for capturing the order effect in human decision making, compared with the classical probability model. A central decision maker (CDM) receives (possibly a subset of) the observers' decisions and decides on the true hypothesis. The considered problem becomes an optimal decision fusion problem with observations modeled using a non-commutative (Von Neumann) probability model. The structure of the optimal decision rule at the CDM is studied under two scenarios. In the first scenario, the CDM receives the entire history of the observers' decisions whereas in the second scenario, the CDM receives only the last decision of each observer. The perfromance of the optimal fusion rule is numerically evaluated and compared with the optimal fusion rule derived when using a classical probability model.

We consider an extension of the Rescorla-Wagner model which bridges the gap between conditioning and learning on a neural-cognitive, individual psychological level, and the social population level. In this model, the interaction among individuals is captured by a Markov process. The resulting human-social behavior model is a recurrent iterated function system which behaves differently from the classical Rescorla-Wagner model due to randomness. A sufficient condition for the convergence of the forward process starting with arbitrary initial distribution is provided. Furthermore, the ergodicity properties of the internal states of agents in the proposed model are studied.

This paper presents a learning-based approach for disturbance attenuation for a non-linear dynamical system with event-based observer and model predictive control (MPC). Using the empirical risk minimization (ERM) method, we can obtain a learning error bound which is function of the number of samples, learning parameters, and model complexity. It enables us to analyze the closed-loop stability in terms of the learning property, where the state estimation error by the ERM learning is guaranteed to be bounded. Simulation results underline the learning's capability, the control performance and the event-triggering efficiency in comparison to the conventional event-triggered control scheme.

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.