arXiv subjects

Quanyan Zhu

Publications and source records attributed to Quanyan Zhu.

At least 163 records · Page 9Linked to original sources

arXiv2021

On the Differential Private Data Market: Endogenous Evolution, Dynamic Pricing, and Incentive Compatibility

Privacy is an essential issue in data trading markets. This work uses a mechanism design approach to study the optimal market model to economize the value of privacy of personal data, using differential privacy. The buyer uses a finite number of randomized algorithms to get access to the owners' data in a sequential-composition manner, in which each randomized algorithm is differentially private. Each usage of a randomized algorithm is referred to as a period. Due to the composability of differential privacy, there are inevitable privacy losses accumulated over periods. Hence, we allow the owners to leave the market at the end of any period by making stopping decisions. We define an instrumental kernel function to capture the instrumentalness of owners' preferences and model the formation of each owner's (both intrinsic and instrumental) preference over periods by taking into consideration the composability of differential privacy and the time-varying nature of privacy concerns. Our desideratum is to study the buyer's design regime of optimal market models in a dynamic environment when each owner makes coupled decisions of stopping and reporting of their preferences. The buyer seeks to design a privacy allocation rule that dynamically specifies the degree of privacy protections and a payment rule to compensate for the privacy losses of the owners. The buyer additionally chooses a payment rule which is independent of owners' reports of their preferences to influence the owners' stopping decisions. We characterize the dynamic incentive compatibility and provide a design principle to construct the payment rules in terms of the privacy allocation rule. Further, we relax the buyer's market design problem and provide a sufficient condition for an approximated dynamic incentive-compatible market model.

cs.GT↗

arXiv2021

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision Making: A Review

This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making. We provide a review of a range of epidemic models and explain the pros and cons of different epidemic models. We exhibit the art of coupling epidemic models and decision models in the existing literature. {More specifically, we provide answers to fundamental questions in human decision-making amid epidemics, including what interventions to take to combat the disease, who are decision-makers, when to take interventions, and how to make interventions.} Among many decision models, game-theoretic models have become increasingly crucial in modeling human responses/behavior amid epidemics in the last decade. In this review, we motivate the game-theoretic approach to human decision-making amid epidemics. This review provides an overview of the existing literature by developing a multi-dimensional taxonomy, which categorizes existing literature based on multiple dimensions, including 1) types of games, such as differential games, stochastic games, evolutionary games, and static games; 2) types of interventions, such as social distancing, vaccination, quarantine, taking antidotes, etc.; 3) the types of decision-makers, such as individuals, adversaries, and central authorities at different hierarchical levels. A fine-grained dynamic game framework is proposed to capture the essence of game-theoretic decision-making amid epidemics. We showcase three representative {frameworks} with unique ways of integrating game-theoretic decision-making into the epidemic models from a vast body of literature. {Each of the three framework has a unique way of modeling, conducting analytical analysis, and deriving results.} In the end, we identify several main open problems and research gaps left to be addressed and filled.

eess.SY↗

arXiv2021

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Digitization and remote connectivity have enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an essential tool that epitomizes the feedback architectures for cyber resilience. It allows the CRM to provide sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. We introduce three application domains of CRMs: moving target defense, defensive cyber deception, and assistive human security technologies. The RL algorithms also have vulnerabilities themselves. We explain the three vulnerabilities of RL and present attack models where the attacker targets the information exchanged between the environment and the agent: the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. Lastly, we discuss the future challenges of RL for cyber security and resilience and emerging applications of RL-based CRMs.

cs.CR↗

arXiv2021

Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability

This work proposes a new class of proactive attacks called the Informational Denial-of-Service (IDoS) attacks that exploit the attentional human vulnerability. By generating a large volume of feints, IDoS attacks deplete the cognitive resources of human operators to prevent humans from identifying the real attacks hidden among feints. This work aims to formally define IDoS attacks, quantify their consequences, and develop human-assistive security technologies to mitigate the severity level and risks of IDoS attacks. To this end, we use the semi-Markov process to model the sequential arrivals of feints and real attacks with category labels attached in the associated alerts. The assistive technology strategically manages human attention by highlighting selective alerts periodically to prevent the distraction of other alerts. A data-driven approach is applied to evaluate human performance under different Attention Management (AM) strategies. Under a representative special case, we establish the computational equivalency between two dynamic programming representations to reduce the computation complexity and enable online learning with samples of reduced size and zero delays. A case study corroborates the effectiveness of the learning framework. The numerical results illustrate how AM strategies can alleviate the severity level and the risk of IDoS attacks. Furthermore, the results show that the minimum risk is achieved with a proper level of intentional inattention to alerts, which we refer to as the law of rational risk-reduction inattention.

cs.CR↗

arXiv2021

Duplicity Games for Deception Design with an Application to Insider Threat Mitigation

Recent incidents such as the Colonial Pipeline ransomware attack and the SolarWinds hack have shown that traditional defense techniques are becoming insufficient to deter adversaries of growing sophistication. Proactive and deceptive defenses are an emerging class of methods to defend against zero-day and advanced attacks. This work develops a new game-theoretic framework called the duplicity game to design deception mechanisms that consist of a generator, an incentive modulator, and a trust manipulator, referred to as the GMM mechanism. We formulate a mathematical programming problem to compute the optimal GMM mechanism, quantify the upper limit of enforceable security policies, and characterize conditions on user's identifiability and manageability for cyber attribution and user management. We develop a separation principle that decouples the design of the modulator from the GMM mechanism and an equivalence principle that turns the joint design of the generator and the manipulator into the single design of the manipulator. A case study of dynamic honeypot configurations is presented to mitigate insider threats. The numerical experiments corroborate the results that the optimal GMM mechanism can elicit desirable actions from both selfish and adversarial insiders and consequently improve the security posture of the insider network. In particular, a proper modulator can reduce the \textcolor{black}{incentive misalignment} between the players and achieve win-win situations for the selfish insider and the defender. Meanwhile, we observe that the defender always benefits from faking the percentage of honeypots when the optimal generator is presented.

cs.GT↗

arXiv2021

EPROACH: A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

The COVID-19 lockdowns have created a significant socioeconomic impact on our society. In this paper, we propose a population vaccination game framework, called EPROACH, to design policies for reopenings that guarantee post-opening public health safety. In our framework, a population of players decides whether to vaccinate or not based on the public and private information they receive. The reopening is captured by the switching of the game state. The insights obtained from our framework include the appropriate vaccination coverage threshold for safe-reopening and information-based methods to incentivize individual vaccination decisions. In particular, our framework bridges the modeling of the strategic behaviors of the populations and the spreading of infectious diseases. This integration enables finding the threshold which guarantees a disease-free epidemic steady state under the population's Nash equilibrium vaccination decisions. The equilibrium vaccination decisions depend on the information received by the agents. It makes the steady-state epidemic severity controllable through information. We find out that the externalities created by reopening lead to the coordination of the rational players in the population and result in a unique Nash equilibrium. We use numerical experiments to corroborate the results and illustrate the design of public information for responsible reopening.

cs.GT↗

arXiv2021

Cybersecurity in Robotics: Challenges, Quantitative Modeling, and Practice

Robotics is becoming more and more ubiquitous, but the pressure to bring systems to market occasionally goes at the cost of neglecting security mechanisms during the development, deployment or while in production. As a result, contemporary robotic systems are vulnerable to diverse attack patterns, and an a posteriori hardening is at least challenging, if not impossible at all. This book aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-benefit tradeoff that can otherwise be an inhibitor for the fast development of affordable systems. We advocate quantitative methods of security management and design, covering vulnerability scoring systems tailored to robotic systems, and accounting for the highly distributed nature of robots as an interplay of potentially very many components. A powerful quantitative approach to model-based security is offered by game theory, providing a rich spectrum of techniques to optimize security against various kinds of attacks. Such a multi-perspective view on security is necessary to address the heterogeneity and complexity of robotic systems. This book is intended as an accessible starter for the theoretician and practitioner working in the field.

cs.RO↗

arXiv2021

Efficient Episodic Learning of Nonstationary and Unknown Zero-Sum Games Using Expert Game Ensembles

Game theory provides essential analysis in many applications of strategic interactions. However, the question of how to construct a game model and what is its fidelity is seldom addressed. In this work, we consider learning in a class of repeated zero-sum games with unknown, time-varying payoff matrix, and noisy feedbacks, by making use of an ensemble of benchmark game models. These models can be pre-trained and collected dynamically during sequential plays. They serve as prior side information and imperfectly underpin the unknown true game model. We propose OFULinMat, an episodic learning algorithm that integrates the adaptive estimation of game models and the learning of the strategies. The proposed algorithm is shown to achieve a sublinear bound on the saddle-point regret. We show that this algorithm is provably efficient through both theoretical analysis and numerical examples. We use a dynamic honeypot allocation game as a case study to illustrate and corroborate our results. We also discuss the relationship and highlight the difference between our framework and the classical adversarial multi-armed bandit framework.

cs.GT↗

arXiv2021

A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion

This article studies rational and persistent deception among intelligent robots to enhance security and operational efficiency. We present an N-player K-stage game with an asymmetric information structure where each robot's private information is modeled as a random variable or its type. The deception is persistent as each robot's private type remains unknown to other robots for all stages. The deception is rational as robots aim to achieve their deception goals at minimum cost. Each robot forms a dynamic belief of others' types based on intrinsic or extrinsic information. Perfect Bayesian Nash equilibrium (PBNE) is a natural solution concept for dynamic games of incomplete information. Due to its requirements of sequential rationality and belief consistency, PBNE provides a reliable prediction of players' actions, beliefs, and expected cumulative costs over the entire K stages. The contribution of this work is fourfold. First, we identify the PBNE computation as a nonlinear stochastic control problem and characterize the structures of players' actions and costs under PBNE. We further derive a set of extended Riccati equations with cognitive coupling under the linear-quadratic (LQ) setting and extrinsic belief dynamics. Second, we develop a receding-horizon algorithm with low temporal and spatial complexity to compute PBNE under intrinsic belief dynamics. Third, we investigate a deceptive pursuit-evasion game as a case study and use numerical experiments to corroborate the results. Finally, we propose metrics, such as deceivability, reachability, and the price of deception (PoD), to evaluate the strategy design and the system performance under deception.

eess.SY↗

arXiv2021

A Dynamic Game Approach to Designing Secure Interdependent IoT-Enabled Infrastructure Network

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous interdependent networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT-enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining network connectivity in the presence of adversaries is a critical task for infrastructure network operators. In this paper, we establish a three-player three-stage dynamic game-theoretic framework including two network operators and one attacker to capture the secure design of multi-layer interdependent infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of the Internet of Battlefield Things (IoBT) to corroborate the obtained results.

cs.NI↗

arXiv2021

Herd Behaviors in Epidemics: A Dynamics-Coupled Evolutionary Games Approach

The recent COVID-19 pandemic has led to an increasing interest in the modeling and analysis of infectious diseases. The pandemic has made a significant impact on the way we behave and interact in our daily life. The past year has witnessed a strong interplay between human behaviors and epidemic spreading. In this paper, we propose an evolutionary game-theoretic framework to study the coupled evolutions of herd behaviors and epidemics. Our framework extends the classical degree-based mean-field epidemic model over complex networks by coupling it with the evolutionary game dynamics. The statistically equivalent individuals in a population choose their social activity intensities based on the fitness or the payoffs that depend on the state of the epidemics. Meanwhile, the spreading of the infectious disease over the complex network is reciprocally influenced by the players' social activities. We analyze the coupled dynamics by studying the stationary properties of the epidemic for a given herd behavior and the structural properties of the game for a given epidemic process. The decisions of the herd turn out to be strategic substitutes. We formulate an equivalent finite-player game and an equivalent network to represent the interactions among the finite populations. We develop structure-preserving approximation techniques to study time-dependent properties of the joint evolution of the behavioral and epidemic dynamics. The resemblance between the simulated coupled dynamics and the real COVID-19 statistics in the numerical experiments indicates the predictive power of our framework.

cs.GT↗

arXiv2021

Informational Design of Dynamic Multi-Agent System

This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by a Markov game, in which each agent first selects one external signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (principal) who possesses one signal source and aims to influence the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. We propose a direct information design approach that incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer which we refer to as obedient implementability (OIL) and characterize the OIL in a class of obedient sequential Markov perfect equilibria (O-SMPE). A design regime is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, guarantees that the agents' policy profile of taking actions is the policy component of an O-SMPE and the principal's goal is achieved. We then formulate the principal's optimal goal selection problem in terms of information design and characterize the optimization problem by minimizing the fixed-point misalignments. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.

cs.MA↗

arXiv2021

Feedback Capacity and a Variant of the Kalman Filter with ARMA Gaussian Noises: Explicit Bounds and Feedback Coding Design

In this paper, we relate a feedback channel with any finite-order autoregressive moving-average (ARMA) Gaussian noises to a variant of the Kalman filter. In light of this, we obtain relatively explicit lower bounds on the feedback capacity for such colored Gaussian noises, and the bounds are seen to be consistent with various existing results in the literature. Meanwhile, this variant of the Kalman filter also leads to explicit recursive coding schemes with clear structures to achieve the lower bounds. In general, our results provide an alternative perspective while pointing to potentially tighter bounds for the feedback capacity problem.

cs.IT↗

arXiv2021

Fundamental Limits of Prediction, Generalization, and Recursion: An Entropic-Innovations Perspective

In this paper, we examine the fundamental performance limits of prediction, with or without side information. More specifically, we derive generic lower bounds on the $\mathcal{L}_p$ norms of the prediction errors that are valid for any prediction algorithms and for any data distributions. Meanwhile, we combine the entropic analysis from information theory and the innovations approach from prediction/estimation theory to characterize the conditions (in terms of, e.g., directed information or mutual information) to achieve the bounds. We also investigate the implications of the results in analyzing the fundamental limits of generalization in fitting (learning) problems from the perspective of prediction with side information, as well as the fundamental limits of recursive algorithms by viewing them as generalized prediction problems.

cs.LG↗

arXiv2021

Fundamental Limits of Controlled Stochastic Dynamical Systems: An Information-Theoretic Approach

In this paper, we examine the fundamental performance limitations in the control of stochastic dynamical systems; more specifically, we derive generic $\mathcal{L}_p$ bounds that hold for any causal (stabilizing) controllers and any stochastic disturbances, by an information-theoretic analysis. We first consider the scenario where the plant (i.e., the dynamical system to be controlled) is linear time-invariant, and it is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the conditional entropy of the disturbance. We then analyze the setting where the plant is assumed to be (strictly) causal, for which case the lower bounds are determined by the conditional entropy of the disturbance. We also discuss the special cases of $p = 2$ and $p = \infty$, which correspond to minimum-variance control and controlling the maximum deviations, respectively. In addition, we investigate the power-spectral characterization of the lower bounds as well as its relation to the Kolmogorov-Szegö formula.

eess.SY↗

arXiv2021

Control Challenges for Resilient Control Systems

In this chapter, we introduce methods to address resiliency issues for control systems. The main challenge for control systems is its cyber-physical system nature which strongly couples the cyber systems with physical layer dynamics. Hence the resiliency issues for control systems need to be addressed by integrating the cyber resiliency with the physical layer resiliency. We introduce frameworks that can provide a holistic view of the control system resiliency and a quantitative design paradigm that can enable an optimal cross-layer and cross-stage design at the planning, operation, and recovery stage of control systems. The control systems are often large-scale systems in industrial application and critical infrastructures. Decentralized control of such systems is indispensable. We extend the resiliency framework to address distributed and collaborative resiliency among decentralized control agents.

eess.SY↗

arXiv2021

Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective

In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the "innovation" is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szegö formula and Wiener-Masani formula known from linear prediction theory.

cs.IT↗

arXiv2021

Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis

In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

cs.LG↗