Information sharing vs. privacy: A game theoretic analysis

Sharing cyber security information helps firms to decrease cyber security risks, prevent attacks, and increase their overall resilience. Hence it affects reducing the social security cost. Although previously cyber security information sharing was being performed in an informal and ad hoc manner, nowadays through development of information sharing and analysis centers (ISACs), cyber security information sharing has become more structured, regular, and frequent. This is while, the privacy risk and information disclosure concerns are still major challenges faced by ISACs that act as barriers in activating the potential impacts of ISACs.This paper provides insights on decisions about security investments and information sharing in consideration of privacy risk and security knowledge growth. By the latest concept i.e. security knowledge growth, we mean fusing the collected security information, adding prior knowledge, and performing extra analyses to enrich the shared information. The impact of this concept on increasing the motivation of firms for voluntarily sharing their sensitive information to authorities such as ISACs has been analytically studied for the first time in this paper. We propose a differential game model in which a linear fusion model for characterizing the process of knowledge growth via the ISAC is employed. The Nash equilibrium of the proposed game including the optimized values of security investment, and the thresholds of data sharing with the price of privacy are highlighted. We analytically find the threshold in which the gain achieved by sharing sensitive information outweighs the privacy risks and hence the firms have natural incentive to share their security information. Moreover, since in this case the threshold of data sharing and the security investment levels chosen in Nash equilibrium may be lower than social optimum, accordingly we design mechanisms which would encourage the firms and lead to a socially optimal outcome. The direct impact of the achieved results is on analyzing the way ISACs can convince firms to share their security information with them.     

Unfolding the privacy paradox among mobile social commerce users: a multi-mediation approach

Considering that privacy paradox is currently an underexplored research area, this study proposes a privacy paradox model to explain the reasons behind such phenomenon. Built upon the theoretical link of belief-intention-behaviour and the idea of multi-mediation, this study seeks to discover the relationship between concern for social media information privacy and usage intention, and the potential mediators that stand between such linkage. The results suggest that perceived mobility and social support do mediate the said linkage, and the model could reasonably explain usage intention, which surprisingly has a negative association with usage behaviour. Theoretical and practical implications are subsequently offered.     

Designing free software samples: a game theoretic approach

We develop a vertical differentiation game-theoretic model that addresses the issue of designing free software samples (shareware) for attaining follow-on sales. When shareware can be reinstalled, cannibalization of sales of the commercial product may ensue. We analyze the optimal design of free software according to two characteristics: the evaluation period allotted for sampling (potentially renewable) and the proportion of features included in the sample. We introduce a new software classification scheme based on the characteristics of the sample that aid consumer learning. We find that the optimal combination of features and trial time greatly depends on the category of software within the classification scheme. Under alternative learning scenarios, we show that the monopolist may be better off not suppressing potential shareware reinstallation.

Service discovery in mobile ad hoc networks: A field theoretic approach

Service discovery in mobile ad hoc networks is challenging because of the absence of any central intelligence in the network. Traditional solutions as used in the Internet are hence not well suited for mobile ad hoc networks. In this paper, we present a novel decentralized service discovery mechanism for ad hoc networks. The basic idea is to distribute information about available services to the network neighborhood. We achieve this by using the analogy of an electrostatic field: A service is modelled by a (positive) point charge, and service request packets are seen as (negative) test charges which are attracted by the service instances. In our approach, we map the physical model to a mobile ad hoc network in a way where each network element calculates a potential value and routes service requests towards the neighbor with the highest potential, hence towards a service instance. Our approach allows for differentiation of service instances based on their capacity. We define the required protocols and methods which we implemented in a network simulator. Using extensive simulations, we evaluate the performance and robustness of the mechanisms. The results indicate good performance and convergence even in highly mobile environments. We believe that this technique can and should be further exploited, e.g., as a routing protocol in mobile ad hoc networks.     

Channel assignment in heterogeneous multi-radio multi-channel wireless networks: A game theoretic approach

Channel assignment is a challenging issue for multi-radio multi-channel wireless networks, especially in a competing environment. This paper investigates channel assignment for selfish nodes in a heterogeneous scenario, in which nodes may have different QoS requirements and thus compete for different channels with unequal bandwidth. The interaction among nodes is formulated as a non-cooperative Multi-radio Channel Assignment Game (MCAG), where Nash Equilibrium (NE) corresponds to a stable channel assignment outcome from which no individual node has the incentive to deviate. The NEs in MCAG are characterized in this paper. Since multiple NEs may exist in this game, it is natural to choose the NE that maximizes the network utility, i.e., the sum of node utilities. It is shown that the optimal NE outcome can be derived by solving an integer non-linear programming problem. Based on some observations on the radio number distribution of NE, we propose a two-stage optimization algorithm to achieve an optimal channel assignment. Finally, computer simulations validate the effectiveness of the proposed algorithm.     

Sensor fault detection and isolation: a game theoretic approach

This paper studies sensor fault detection using a game theoretic approach. Sensor fault detection is considered as change point analysis in the coefficients of a regression model. A new method for detecting faults, referred to as two-way fault detection, is introduced which defines a game between two players, i.e. the fault detectors. In this new strategic environment, assuming that the independent states of the regression model are known, the test statistics are derived and their finite sample distributions under the null hypothesis of no change are derived. These test statistics are useful for testing the fault existence, as well as, the pure and mixed Nash equilibriums are derived for at-most-one-change and epidemic change models. A differential game is also proposed and solved using the Pontryagin maximum principle. This solution is useful for studying the fault detection problem in unknown state cases. Kalman filter and linear matrix inequality methods are used in finding the Nash equilibrium for the case of unknown states. Illustrative examples are presented to show the existence of the Nash equilibriums. Also, the proposed fault detection scheme is numerically evaluated via its application on a practical system and its performance is compared with the cumulative sum method.     

Living in a big data world: Predicting mobile commerce activity through privacy concerns

As advertisers increasingly rely on mobile-based data, consumer perceptions regarding the collection and use of such data becomes of great interest to scholars and practitioners. Recent industry data suggests advertisers seeking to leverage personal data offered via mobile devices would be wise to acknowledge and address the privacy concerns held by mobile users. Utilizing the theoretical foundation of communication privacy management (CPM), the current study investigates commonly understood privacy concerns such as collection, control, awareness, unauthorized secondary use, improper access and a newly adapted dimension of location tracking, trust in mobile advertisers, and attitudes toward mobile commerce, to predict mobile commerce engagement. Data from this study indicate that control, unauthorized access, trust in mobile advertisers, and attitude toward mobile commerce significantly predicted 43% of the variance in mobile commerce activity.     

Data Fusion-based resilient control system under DoS attacks: A game theoretic approach

In this paper, the resilient control under the Denial-of-Service (DoS) attack is rebuilt within the framework of Joint Directors of Laboratories (JDL) data fusion model. The JDL data fusion process is characterized by the so-called Game-in-Game approach, where decisions are made at different layers. The interactions between different JDL levels are considered which take the form of Packet Delivery Rate of the communication channel. Some criterions to judge whether the cyber defense system is able to protect the underlying control system is provided. Finally, a numerical example is proposed to verify the validity of the proposed method.     

A game theoretic approach to optimal control in the presence of uncertainty

The optimal control problem, in the presence of uncertainty in the plant, is formulated as a game between the uncertainty and the control variables. This approach gives an optimal control strategy which is effective even under the "worst" conditions of uncertainty. The optimal control of a second-order plant with uncertainty in frequency brings out several interesting features. In particular, the existence of a barrier demarcating the controllable and uncontrollable regions in the phase plane is revealed, which is absent in the corresponding one-sided optimization.     

A game theoretic approach to the optimal control of uncertain systems

The issue of performance robustness is considered in a dynamic games framework where the goal is to find a feedback controller that stabilizes the system under all plant perturbations and that minimizes the value of a quadratic performance index under the worst perturbations. To find a solution to this hard-constrained problem, an auxiliary soft-constrained dynamic game problem is formulated. It is shown that the solution to the auxiliary problem provides the solution to the original problem, under the assumption that the solution to the original problem occurs on the boundary of the perturbations set. The approach, however, can still be applied, with modifications, to the case when this assumption fails.     

Planning for dynamic multiagent planar manipulation with uncertainty: a game theoretic approach

This paper addresses the planning problem for multiagent dynamic manipulation in the plane. The objective of planning is to design the forces exerted on the object by agents with which the object can follow a given trajectory in spite of the uncertainty on pressure distribution. The main novelty of the proposed approach is the integration of noncooperative and cooperative games between agents in an hierarchical manner. Based on a dynamic model of the pushed object, the coordination problem is solved in two levels. In the lower control level, a fictitious force controller is designed by using a minimax technique to achieve the tracking performance. The design procedure is divided into two steps. First, a linear nominal controller is designed via full-state linearization with desired eigenvalues assignment. Next, a minimax control scheme is specified to optimally attenuate the worst-case effect of the uncertainty due to pressure distribution and achieve a minimax tracking performance. In the coordination level, a cooperative game is formulated between agents to distribute the fictitious force, and the objective of the game is to minimize the worst-case interaction force between agents and the object. Simulations are carried out for two-agent and three-agent manipulations, results demonstrate the effectiveness of the planning method.     

A game theoretic approach for power allocation with QoS constraints in wireless multimedia sensor networks

The distinctive features of wireless multimedia sensor networks (WMSNs) include application-specific quality-of-service (QoS) requirements and limited energy supply, with which each node makes its own decisions selfishly. Therefore this paper presents a power control game theoretic approach for WMSNs by studying the effect of transmission power on QoS and energy efficiency. The game approach determines the transmission strategy using utility optimization according to the fluctuation of channel states. Here, the utility function is defined by effective throughput per unit power while satisfying the user’s delay QoS constraints. The existence and uniqueness of Nash equilibrium for the proposed game are proved. Finally, the simulation results show that each user chooses the optimal transmission power to maximize its utility based on other constant parameters and the effects of delay constraints on the user’s utility are quantified as well.     

A sequential cooperative game theoretic approach to scheduling multiple large-scale applications in grids

Scheduling large-scale applications in heterogeneous distributed computing systems is a fundamental NP-complete problem that is critical to obtaining good performance and execution cost. In this paper, we address the scheduling problem of an important class of large-scale Grid applications inspired by the real world, characterized by a huge number of homogeneous, concurrent, and computationally intensive tasks that are the main sources of performance, cost, and storage bottlenecks. We propose a new formulation of this problem based on a cooperative distributed game-theory-based method applied using three algorithms with low time complexity for optimizing three important metrics in scientific computing: execution time, economic cost, and storage requirements. We present comprehensive experiments using simulation and real-world applications that demonstrate the effectiveness of our approach in terms of time and fairness compared to other related algorithms.     

A game theoretic fault detection filter

The fault detection process is approximated with a disturbance attenuation problem. The solution to this problem, for both linear time-varying and time-invariant systems, leads to a game theoretic filter which bounds the transmission of all exogenous signals except the fault to be detected. In the limit, when the disturbance attenuation bound is brought to zero, a complete transmission block is achieved by embedding the nuisance inputs into an unobservable, invariant subspace. Since this is the same invariant subspace structure seen in some types of detection filters, we can claim that the asymptotic game filter is itself a detection filter. One can also make use of this subspace structure to reduce the order of the limiting game theoretic filter by factoring this invariant subspace out of the state space. The resulting lower dimensional filter will then be sensitive only to the failure to be detected. A pair of examples given at the end of the paper demonstrate the effectiveness of the filter for time-invariant and time-varying problems in both full-order and reduced-order forms     

Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

We develop a novel auction-based scheme for trading free processors in cloud computing environment. Our scheme allows clients to fairly compete for the offered processors. Second-price auction is used where each client bids for the processors. The client that makes the higher bid wins the auction and starts using the processors for executing its jobs. The winner pays the second highest bid. Maximising profit is the key objective for the cloud provider (CP). The optimisation problem of renting processors on the cloud market is a complex and challenging task. The proposed mechanism takes into account the market demand and the economic concerns for the CP and clients. Within this context, we show the existence of a Nash equilibrium which leads to a unique allocation under certain processors distribution. Numerical analysis stresses the ability of our approach to maximise the client's utility under varying cloud environment conditions.     

Determining the mobile commerce user requirements using an analytic approach

Independent studies have shown that mobile commerce (m-commerce) can have an important influence on business and society in the future. Hence, network designers, service providers, vendors and application developers must carefully take the needs and considerations of various users into account to provide better services and attract them to m-commerce. Consequently, identifying the m-commerce user requirements and their significance becomes an essential and crucial process for the standardization and improvement of associated systems. On this line, the objective of this paper is to propose an analytic framework to provide practitioners a more effective and efficient model for prioritizing m-commerce requirements.     

QoS-aware bandwidth allocation and admission control in IEEE 802.16 broadband wireless access networks: A non-cooperative game theoretic approach

In this paper, we propose an adaptive bandwidth allocation and admission control mechanism based on game theory for IEEE 802.16 broadband wireless networks. A non-cooperative two-person non-zero-sum game is formulated where the base station and a new connection are the players of this game. The solution of the game formulation provides not only the decision on accepting or rejecting a connection, but also the amount of bandwidth allocated to a new connection (if admitted). A queueing model considering adaptive modulation and coding in the physical layer is used to analyze quality of service (QoS) performances, namely, the delay performance for real-time and the throughput performance for non-real-time polling services and best effort service. This queueing model is used by the proposed bandwidth allocation and admission control mechanism to ensure that the utilities for both the base station and the new connection are maximized. The performance of the proposed scheme is evaluated by simulation and compared with that of each of the traditional admission control with static and adaptive bandwidth allocation schemes.     

Replicating data objects in large distributed database systems: an axiomatic game theoretic mechanism design approach

Data object replication onto distributed servers can potentially alleviate bottlenecks, reduce network traffic, increase scalability, add robustness, and decrease user perceived access time. The decision of selecting data object and server pairs requires solving a constraint optimization problem that in general is NP-complete. In this paper, we abstract the distributed database system as an agent-based model, wherein agents continuously compete for allocation and reallocation of data objects. Each agent aims to replicate objects onto its server such that the communication cost is minimized. However, these agents do not have a global view of the system. Thereby, the optimization process becomes highly localized. Such localized optimization may severely affect the overall system performance. To cope with such localized optimization, we propose a “semi-distributed” axiomatic game theoretical mechanism. The mechanism’s control is unique in its decision making process, wherein all the heavy processing is done on the servers of the distributed system and the central body is only required to take a binary decision: (0) not to replicate or (1) to replicate. The cost model used by the agents in the mechanism for the purpose of identifying beneficial data objects is tailored made so that even though the agents take decisions based on their local knowledge domain, the effect is translated into a system-wide performance enhancement. The proposed mechanism is extensively compared against seven well-known conventional and three game theoretical replica allocation methods, namely, branch and bound, greedy, genetic, data-aware replication, tree inspired bottom-up procedure, tree inspired min-max procedure, Benders’ decomposition based procedure, game theoretical English auction, game theoretical Dutch auction, and game theoretical selfish replication procedure. The experimental setup incorporates GT-ITM, Inet network topology generators, Soccer World Cup 1998 access logs, and NASA Kennedy Space Center access logs to closely mimic the Web in its infrastructure and user access patterns. The experimental results reveal that the proposed technique despite its non-cooperative nature improves the solution quality and reduces the execution time compared to other techniques.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

A game theoretic approach for the distributed control of multi-agent systems under directed and time-varying topology

This paper presents a new game theory based method to control multi-agent systems under directed and time varying interaction topology. First, the sensing/communication matrix is introduced to cope with information sharing among agents, and to provide the minimal information requirement which ensures the system level objective is desirable. Second, different from traditional methods of controlling multi-agent systems, non-cooperative games are investigated to enforce agents to make rational decisions. And a new game model, termed stochastic weakly acyclic game, is developed to capture the optimal solution to the distributed optimization problem for multi-agent systems with directed topology. It is worth noting that the system level objective can be achieved at the points of the corresponding equilibriums of the new game model. The proposed method is illustrated with an example in smart grid where multiple distributed generators are controlled to reach the fair power utilization profile in the game formulation to ensure the aggregated power output are optimal.