Bayesian Stackelberg Game Research Articles

A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.

A One-Leader Multi-Follower Bayesian-Stackelberg Game for Anti-Jamming Transmission in UAV Communication Networks

This paper focuses on the anti-jamming transmission problem in unmanned aerial vehicle (UAV) communication networks. Considering the incomplete information constraint and the co-channel mutual interference, a Bayesian Stackelberg game is proposed to formulate the competitive relations between UAVs (users) and the jammer. Specifically, the jammer acts as the leader, whereas users act as followers of the proposed game. Based on their utility functions, the jammer and users select their optimal power control strategies respectively. In addition, the observation error is also investigated in this paper. Due to the incomplete information constraint and the existence of co-channel mutual interference, it is challenging to obtain Stackelberg equilibrium (SE) of the proposed game. Thus, a sub-gradient based Bayesian Stackelberg iterative algorithm is proposed to obtain SE. Finally, simulations are conducted to demonstrate the performance of the proposed algorithm, and the impact of incomplete information and observation error on users’ utilities are also presented.

MTD-Spamguard: a moving target defense-based spammer detection system in social network

Machine learning classifiers are currently the state of the art for spammer detection tasks in SNSs. Note, however, that these classifiers fail to detect adaptive spammers that dynamically change their spamming strategies or behaviors and attempt to pose as legitimate users. In this paper, we propose an efficient spammer detection system (which we call MTD-Spamguard) wherein the notion of MTD is applied to increase the robustness of well-known machine learning classifiers against the adaptive spammers in SNSs. The system introduces a new method of MTD wherein the concept of differential immunity of different classifiers is employed to detect the spammers. To classify a single user in the test dataset, we pick one of the appropriate trained classifiers from multiple classifiers and then use its classification output. To choose the appropriate classifier, we design an effective classifier switching strategy by formulating the interaction of users (normal users and spammers) and detector (which hosts the machine learning classifier) as a repeated Bayesian Stackelberg game. The classifier switching strategy provides strong Stackelberg equilibrium between users and detector, maximizing the accuracy of classification and reducing the misclassification of spammers. The system achieves 30% gain in classification accuracy over the Facebook dataset (constructed in our recent work).

Applying a Bayesian Stackelberg game for securing a chemical plant

Applying a Bayesian Stackelberg game for securing a chemical plant

Protecting trajectory privacy: A user-centric analysis

The existing evaluation methods for location privacy protection mechanism (LPPM) focus mainly on location privacy at the time of issuing a location-based query, and rarely consider trajectory privacy from where multiple successive queries are issued. This paper provides a user-centric analysis of trajectory privacy protection against tracking attacks in presence of quality-loss and energy constraints, focusing on impact of user query behavior within a certain period of time. We consider an attacker with adversarial knowledge on users’ activity and mobility profiles to run tracking attacks, against which users employ LPPMs that incorporate location semantic diversity to maximize trajectory privacy. The attack and defense problems are formalized as a Bayesian Stackelberg game for quantitative analysis. Real location traces are used to assess the LPPMs’ effectiveness, and to find optimal query strategy. The results confirm that query behavior has a significant impact on improving trajectory privacy. Despite increased number of queries to improve trajectory privacy, the effectiveness may be decreased with a longer observation time. At the same query rate, prolonging observation time is beneficial for privacy improvement. We also find that dummy-based LPPM often provides better protection with a stringent energy constraint, while precision-based LPPM performs better under a stringent quality-loss constraint or under a loose energy constraint. At optimal trajectory privacy, quality-loss of precision-based LPPM is lower than that of dummy-based LPPM, but there is an opposite result in terms of energy cost.

Bayesian Stackelberg Game for Antijamming Transmission With Incomplete Information

The smart jammers pose a severe threat to wireless communications due to their abilities of learning the users’ transmission strategies. A Stackelberg game can be formulated to model and analyze the hierarchical interactions between the user and the smart jammer. In this letter, an antijamming Bayesian Stackelberg game with incomplete information is proposed. In the proposed game, the user who acts as the leader has the privilege over the smart jammer and takes actions first, whereas the smart jammer acting as the follower moves subsequently. Moreover, the Stackelberg equilibrium (SE) is derived, and the existence and the uniqueness of SE are demonstrated. Simulation results are presented to validate the effectiveness of the proposed antijamming Bayesian Stackelberg game.

When to offload in two-tier smallcell networks: a Stackelberg game approach with pre-offloading-decision

In this paper, we consider the problem of traffic offloading in a two-tier downlink smallcell network with hybrid access. We formulate a Stackelberg game with pre-offloading decision, which includes two steps, to study the problem. The utility of the MBS is designed as a piecewise function, corresponding to the two possible decisions of the MBS. We design a bonus-based incentive mechanism to motivate smallcell base stations (SBSs) serve the MUEs. We prove the existence and uniqueness of the Stackelberg equilibrium. We also consider the incomplete information game, where the SBSs have private information. A Bayesian Stackelberg game with pre-offloading decision is formulated and the existence of Bayesian Stackelberg equilibrium is proved. Our proposed approach achieves much higher utilities than the existing approach does under some circumstances in both cases, which proves the necessity and superiority of our proposed game structure.

Two-way information sharing under supply chain competition

We study manufacturer-retailer bilateral information sharing in two competing supply chains (SCs), in which both the manufacturer and the retailer have partial information on demand. Based on Bertrand competition model and Winkler's consensus model, we develop a finite Bayesian Stackelberg game to analyze the two-way information sharing problem under horizontal supply chain (SC) competition. In line with the literature, we find that sharing demand forecast voluntarily in a SC benefits the manufacturer but hurts the retailer. However, we find whether SCs benefit from information sharing depends on competition intensity and forecast error. As competition is intensive, the expected values of information sharing (EVISs) for the entire SCs are high. Moreover, information sharing in one supply chain can improve the rival supply chain's EVIS under some conditions. Numerical experiments are conducted to get some managerial insights.

Stackelberg Bayesian Game for Power Allocation in Two-Tier Networks

The downlink power allocation in a two-tier cellular network that consists of a macrocell network underlaid by multiple femtocell networks is addressed in this paper. This paper aims to maximize the transmission capacity of the femtocell networks while guaranteeing that the interference experienced at the macro base station does not exceed an interference constraint. We formulate a Bayesian Stackelberg game to model and analyze behaviors of macrocell and femtocell base stations (MBS and FBSs, respectively). In this game, the MBS is the leader, whereas the FBSs are the followers. The channel information between an FBS and its associated femtocell user is private information and is considered the type of the follower. The leader issues the price of interference charged to the followers first to maximize its own profit. Based on the price, the followers decide the strategies to maximize their payoffs defined as the difference between the transmission capacity and the cost of interference (CoI) paid to the leader. Using backward induction, we first analyze the follower game. The existence and uniqueness of the Bayesian Nash equilibrium (BNE) are examined, and the methods to obtain the BNE for a symmetric case are provided. Then, the leader game is analyzed. Finally, the numerical analysis is provided.

A Secure Radio Environment Map Database to Share Spectrum

A robust and secure database for spectrum sharing in cognitive radio networks that is obscured from the viewpoint of secondary users is presented. The database allocations secure features of white space resource usage from being learned. The design of non-inferable database is based on two cases. In the first case, the primary or spectrum lender has no knowledge of secondary users or potential jammers among them. In this case, the problem is modeled as a Markov decision process. In the second case, the primary system has some knowledge about spectrum borrowers and the problem is modeled as a Bayesian Stackelberg game. Mutual information interpretation of the Bayesian Stackelberg game is also presented. The solutions facilitate releasing more bandwidth as required by US National Broadband Plan. Applications of this scheme are manifold. This design can be used for securing spectrum resources, for example radar white spaces, while being shared with LTE and commercial communication systems. Further, it provides jammer-proof spectrum sharing among various communication, detection, and navigation systems. Simulation results verify this scheme improves system throughput while maintaining desired obfuscation level or entropy.

Quantifying and protecting location privacy

Abstract This thesis addresses the timely concern of protecting privacy in the age of big data. We identify the two following problems as the fundamental problems in computational privacy: (i) consistently quantifying privacy in different systems and (ii) optimally protecting privacy using obfuscation mechanisms. We cast the problem of quantifying privacy as computing the estimation error in a statistical (Bayesian) inference problem, where an adversary combines his observation, background knowledge and side channel information to estimate the user's sensitive information. This enables us to evaluate privacy of users in different systems, and consistently compare the effectiveness of different privacy protection mechanisms. We also formulate the problem of optimizing user privacy while respecting data utility as an interactive optimization problem (Bayesian Stackelberg game), where both user and adversary want to maximize their own objectives which are in conflict with each other. We apply our methodologies to quantifying and protecting location privacy in location-based services. We also provide an open-source tool, named Location-Privacy and Mobility Meter (LPM), that enables researchers to learn and analyze human mobility models as well as evaluating and comparing different location-privacy preserving mechanisms.

Quantifying Adversary Capabilities to Inform Defensive Resource Allocation.

We propose a Bayesian Stackelberg game capable of analyzing the joint effects of both attacker intent and capabilities on optimal defensive strategies. The novel feature of our model is the use of contest success functions from economics to capture the extent to which the success of an attack is attributable to the adversary's capability (as well as the level of defensive investment), rather than pure luck. Results of a two-target example suggest that precise assessment of attacker intent may not be necessary if we have poor estimates of attacker capability.

Quantifying and Protecting Location Privacy

Quantifying and Protecting Location Privacy

Game-Theoretic Patrolling with Dynamic Execution Uncertainty and a Case Study on a Real Transit System

Attacker-Defender Stackelberg security games (SSGs) have emerged as an important research area in multi-agent systems. However, existing SSGs models yield fixed, static, schedules which fail in dynamic domains where defenders face execution uncertainty, i.e., in domains where defenders may face unanticipated disruptions of their schedules. A concrete example is an application involving checking fares on trains, where a defender's schedule is frequently interrupted by fare evaders, making static schedules useless. To address this shortcoming, this paper provides four main contributions. First, we present a novel general Bayesian Stackelberg game model for security resource allocation in dynamic uncertain domains. In this new model, execution uncertainty is handled by using a Markov decision process (MDP) for generating defender policies. Second, we study the problem of computing a Stackelberg equilibrium for this game and exploit problem structure to reduce it to a polynomial-sized optimization problem. Shifting to evaluation, our third contribution shows, in simulation, that our MDP-based policies overcome the failures of previous SSG algorithms. In so doing, we can now build a complete system, that enables handling of schedule interruptions and, consequently, to conduct some of the first controlled experiments on SSGs in the field. Hence, as our final contribution, we present results from a real-world experiment on Metro trains in Los Angeles validating our MDP-based model, and most importantly, concretely measuring the benefits of SSGs for security resource allocation.

Computational Game Theory for Security and Sustainability

Security is a critical concern around the world that arises in protecting our ports, airports, transportation and other critical national infrastructure from adversaries, in protecting our wildlife and forests from poachers and smugglers, and in curtailing the illegal flow of weapons, drugs and money; and it arises in problems ranging from physical to cyber-physical systems. In all of these problems, we have limited security resources which prevent full security coverage at all times; instead, security resources must be deployed intelligently taking into account differences in priorities of targets requiring security coverage, the responses of the attackers to the security posture, and potential uncertainty over the types, capabilities, knowledge and priorities of attackers faced. Game theory, which studies interactions among multiple selfinterested agents, is well-suited to the adversarial reasoning required for security resource allocation and scheduling problems. Casting the problem as a Bayesian Stackelberg game, we have developed new algorithms for efficiently solving such games that provide randomized patrolling or inspection strategies. These algorithms have led to some initial successes in this challenging problem arena, leading to advances over previous approaches in security scheduling and allocation, e.g., by addressing key weaknesses of predictability of human schedulers. These algorithms are now deployed in multiple applications: ARMOR has been deployed at the Los Angeles International Airport (LAX) since 2007 to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals [17]; IRIS, a game-theoretic scheduler for randomized deployment of the US Federal Air Marshals (FAMS) requiring significant scaleup in underlying algorithms, has been in use since 2009 [17]; PROTECT, which schedules the US Coast Guard’s randomized patrolling of ports using a new set of algorithms based on modeling bounded-rational human attackers, has been deployed in the

Scheduling Reputation Maintenance in Agent-based Communities Using Game Theory

In agent-based systems, agents can be organized within groups, called communities, where members are providing similar or complementary services. Agent-based communities of web services is an example of such systems. Managing reputation of each agent and of the whole community is a key issue towards securing this type of systems, where a controller agent is designed to observe and check the behavior of each member to update and maintain the system’s reputation. Scheduling the maintenance activity by deciding about the moment where the check has to be done is still an open problem. Because it is highly expensive, maintenance cannot be done every moment or based on small history of agents’ behaviors. We propose in this paper a scheduling algorithm that helps the controller agent improve the quality of the reputation mechanism, which increases the trust value of users toward the community. The proposed algorithm is based on a class of games called Bayesian Stackelberg. Our Bayesian Stackelberg game is designed between the controller agent and community members, for example agent-based web services. We simulate and compare the efficiency of our algorithm with other stochastic techniques, namely uniform, normal and Poisson distributions. This research draws the lines for future work in the subject of optimizing reputation mechanisms through maintenance in different time intervals.

Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service

The increasing threat of terrorism makes security at major locations of economic or political importance a major concern. Limited security resources prevent complete security coverage, allowing adversaries to observe and exploit patterns in patrolling or monitoring, and enabling them to plan attacks that avoid existing patrols. The use of randomized security policies that are more difficult for adversaries to predict and exploit can counter their surveillance capabilities. We describe two applications, ARMOR and IRIS, that assist security forces in randomizing their operations. These applications are based on fast algorithms for solving large instances of Bayesian Stackelberg games. Police at the Los Angeles International Airport deploy ARMOR to randomize the placement of checkpoints on roads entering the airport and the routes of canine unit patrols within the airport terminals. The Federal Air Marshal Service has deployed IRIS in a pilot program to randomize the schedules of air marshals on international flights. This paper examines the design choices, information, and evaluation criteria that were critical to developing these applications.

Using Game Theory for Los Angeles Airport Security

Security at major locations of economic or political importance is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or monitoring; for example, they can plan an attack avoiding existing patrols. Hence, randomized patrolling or monitoring is important, but randomization must provide distinct weights to different actions based on their complex costs and benefits. To this end, this article describes a promising transition of the latest in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called AR MOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features. It uses the fastest known solver for Bayesian Stackelberg games called DOBSS, where the dominant mixed strategies enable randomization; and its mixed‐initiative‐based interface allows users occasionally to adjust or override the automated schedule based on their local constraints. ARMOR has been successfully deployed since August 2007 at the Los Angeles International Airport (LAX) to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. This article examines the information, design choices, challenges, and evaluation that went into de signing ARMOR.

Mechanism Design for Single Leader Stackelberg Problems and Application to Procurement Auction Design

In this paper, we focus on mechanism design for single leader Stackelberg problems, which are a special case of hierarchical decision making problems in which a distinguished agent, known as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leader</i> , makes the first move and this action is followed by the actions of the remaining agents, which are known as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">followers</i> . These problems are also known as <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leader</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rest</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">follower</i> (SLRF) problems. There are many examples of such problems in the areas of electronic commerce, supply chain management, manufacturing systems, distributed computing, transportation networks, and multiagent systems. The game induced among the agents for these problems is a Bayesian Stackelberg game, which is more general than a Bayesian game. For this reason, classical mechanism design, which is based on Bayesian games, cannot be applied as is for solving SLRF mechanism design problems. In this paper, we extend classical mechanism design theory to the specific setting of SLRF problems. As a significant application of the theory developed, we explore two examples from the domain of electronic commerce- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first-price</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second-price</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electronic</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">procurement</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auctions</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reserve</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prices</i> . Using an SLRF model for these auctions, we derive certain key results using the SLRF mechanism design framework developed in this paper. The theory developed has many promising applications in modeling and solving emerging game theoretic problems in engineering.

Bayesian stackelberg games and their application for security at Los Angeles international airport

article Bayesian stackelberg games and their application for security at Los Angeles international airport Share on Authors: Manish Jain University of Southern California, CA University of Southern California, CAView Profile , James Pita University of Southern California, CA University of Southern California, CAView Profile , Milind Tambe University of Southern California, CA University of Southern California, CAView Profile , Fernando Ordóñez University of Southern California, CA University of Southern California, CAView Profile , Praveen Paruchuri Intelligent Automation Inc., Rockville, MD Intelligent Automation Inc., Rockville, MDView Profile , Sarit Kraus Bar-Ilan University, Israel Bar-Ilan University, IsraelView Profile Authors Info & Claims ACM SIGecom ExchangesVolume 7Issue 2June 2008 Article No.: 10pp 1–3https://doi.org/10.1145/1399589.1399599Online:01 June 2008Publication History 25citation462DownloadsMetricsTotal Citations25Total Downloads462Last 12 Months34Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access