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Secure Multiparty Computation (MPC) offers privacy-preserving computation that could be critical in many health and finance applications. Specifically, two or more parties jointly compute a function on private inputs by following a protocol executed in rounds. The MPC network typically consists of direct peer-to-peer (P2P) connections among parties. However, this significantly increases the computation time as parties need to wait for messages from each other, thus making network communication a bottleneck. Most recent works tried to address the communication efficiency by focusing on optimizing the MPC protocol rather than the underlying network topologies and protocols. In this paper, we propose the MPC over Algorand Blockchain (MPC-ABC) protocol that packs messages into Algorand transactions and utilizes its fast gossip protocol to transmit them efficiently among MPC parties. Our approach, therefore, reduces the delay and complexity associated with the fully connected P2P network while assuring the integrity of broadcasted data. We implemented MPC-ABC and utilized it to outsource the SPDZ (SPDZ—pronounced “Speedz"—is the nickname of the MPC protocol of Damgård et al. in (European Symposium on Research in Computer Security, pp 1–18, 2013)) protocol across multiple Cloud Service Providers (CSP). Experimental results show that our approach outperforms the commonly adopted approaches over the P2P TCP/IP network in terms of the average delay and network complexity. [ABSTRACT FROM AUTHOR]

Gene expression analysis is valuable for cancer type classification and identifying diverse cancer phenotypes. The latest high-throughput RNA sequencing devices have enabled access to large volumes of gene expression data. However, we face several challenges, such as data security and privacy, when we develop machine learning-based classifiers for categorizing cancer types with these datasets. To address these issues, we propose IP3G (I ntelligent P henotype-detection and G ene expression profile G eneration with G enerative adversarial network), a model based on Generative Adversarial Networks. IP3G tackles two major problems: augmenting gene expression data and unsupervised phenotype discovery. By converting gene expression profiles into 2-Dimensional images and leveraging IP3G, we generate new profiles for specific phenotypes. IP3G learns disentangled representations of gene expression patterns and identifies phenotypes without labeled data. We improve the objective function of the GAN used in IP3G by employing the earth mover distance and a novel mutual information function. IP3G outperforms clustering methods like k-Means, DBSCAN, and GMM in unsupervised phenotype discovery, while also surpassing SVM and CNN classification accuracy by up to 6% through gene expression profile augmentation. The source code for the developed IP3G is accessible to the public on GitHub. [ABSTRACT FROM AUTHOR]

Aggregation of resources in space, spectrum, and so on, is the fundamental idea behind many technology building blocks of 5G networks, such as massive multi-input multi-output and carrier aggregation, among others. In this respect, another important dimension for parallelism is the aggregation of diverse radio access technologies (RATs), such as WiFi, LTE, and 5G, in heterogeneous networks (HetRAT networks). Simultaneous multiple RAT connectivity facilitates both users and network operators in achieving even higher data rates and in balancing the traffic load, respectively. In this paper, we review the technological supports for the realization of the multi-RAT connectivity and focus on a general framework for resource allocation in HetRAT systems. Specifically, a distributed algorithm is proposed to allocate, with minimal overhead signaling, the bandwidths of available RATs among users considering both the system operator's objective and the users' demands and constraints. The optimality of the proposed distributed algorithm is further proved, and its convergence is investigated. Finally a test case is introduced in which users are power constrained and the system operator enforces capacity limits and motivates users for a fair load balancing over RATs. Numerical results shows the efficiency of the proposed algorithm and its fast convergence in few iterations. [ABSTRACT FROM AUTHOR]

Highlights • We model neuron and astrocytes interactions under normal and abnormal conditions using dynamic and Bayesian games. • We analyze the functionality of normal and abnormal neurons, using Bayesian game. • We investigate the existence of Nash equilibrium in our proposed models. • Our model explains how neurons treat together in various conditions. • Using our proposed model, we investigate the functionality of neurons and astrocytes in epileptic patients. Abstract A neuron is the fundamental unit of the nervous system and the brain, crucial for transducing information in form of trains of electrical pulses known as action potentials. The connection between neurons is through synapses, enabling communication between neurons. This communication link is one of the key elements in processing of information from a neuron to another neuron. The strength of the synapses may vary over time, a phenomenon known as synaptic plasticity. This is the process by which it is believed memory and learning is governed. Recent studies revealed environmental factors affect the strength of synapses, and the way neurons communicate to each other. This poses the question as to what extent the pre- and post- synaptic neurons sense the environmental changes, and in turn adjust their synaptic link. Here, we model the behavior of an interconnected neuronal network in various environmental conditions as a multi-agent system in a game theoretic framework. We focus on a CA1 lattice subfield as an example plastic neuronal network. Our analysis revealed the neuronal network converges to different equilibria depending on the environmental changes. The model well-predicts the behavior of the network compared to a well-known theoretical model of individual neurons. [ABSTRACT FROM AUTHOR]

Natural selection in cancer often results in the emergence of increasingly malignant tumor cells that display many if not all of the hallmarks of cancer. One of the most important traits acquired during cancer progression is angiogenesis. Tumor cells capable of secreting pro-angiogenic factors can be seen as cooperators where the improved oxygenation, nutrient delivery and waste disposal resulting from angiogenesis could be seen as a public good. Under this view, the relatively costly secretion of molecular signals required to orchestrate angiogenesis would be undertaken exclusively by cooperating tumor cells but the benefits of angiogenesis would be felt by neighboring tumor cells regardless of their contribution to the process. In this work we detail a mathematical model to better understand how clones capable of secreting pro-angiogenic factors can emerge in a tumor made of non-cooperative tumor cells. Given the importance of the spatial configuration of the tumor in determining the efficacy of the secretion of pro-angiogenic factors as well as the benefits of angiogenesis we have developed a spatial game theoretic approach where interactions and public good diffusion are described by graphs. The results show that structure of the population affects the evolutionary dynamics of the pro-angiogenic clone. Specifically, when the benefit of angiogenesis is represented by sigmoid function with regards to the number of pro-angiogenic clones then the probability of the coexistence of pro-angiogenic and angiogenesis-neutral clones increases. Our results demonstrate that pro-angiogenic clone equilibrates into clusters that appear from surrounding vascular tissues towards the center of tumor. These clusters appear notably less dense after anti-angiogenic therapy. [ABSTRACT FROM AUTHOR]

One of the main steps in solid cancers to invade surrounding tissues is degradation of tissue barriers in the extracellular matrix. This operation that leads to initiate, angiogenesis and metastasis to other organs, is essentially consequence of collapsing dynamic balance between matrix metalloproteinases ( MMP ) and tissue inhibitors of metalloproteinases ( TIMP ). In this work, we model the MMP–TIMP interaction in both normal tissue and invasive cancer using evolutionary game theory. Our model explains how invasive cancer cells get the upper hand in MMP–TIMP imbalance scenarios. We investigate dynamics of them over time and discuss stable and nonstable states in the population. Numerical simulations presented here provide the identification of key genotypic features in the tumor invasion and a natural description for phenotypic variability. The simulation results are consistent with the experimental results in vitro observations presented in medical literature. Finally, by the provided results the necessary conditions to inhibit cancer invasion or prolong its course are explained. In this way, two therapeutic approaches with respect to how they could meet the required conditions are considered. [ABSTRACT FROM AUTHOR]

Cancer cells and stromal cells cooperate by exchanging diffusible factors that sustain tumor growth, a form of frequency-dependent selection that can be studied in the framework of evolutionary game theory. In the case of multiple myeloma, three types of cells (malignant plasma cells, osteoblasts and osteoclasts) exchange growth factors with different effects, and tumor-stroma interactions have been analysed using a model of cooperation with pairwise interactions. Here we show that a model in which growth factors have autocrine and paracrine effects on multiple cells, a more realistic assumption for tumor-stroma interactions, leads to different results, with implications for disease progression and treatment. In particular, the model reveals that reducing the number of malignant plasma cells below a critical threshold can lead to their extinction and thus to restore a healthy balance between osteoclast and osteoblast, a result in line with current therapies against multiple myeloma. [ABSTRACT FROM AUTHOR]

This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking. [ABSTRACT FROM AUTHOR]

The role of the immune system in tumor development increasingly includes the idea of cancer immunoediting. It comprises three phases: elimination, equilibrium, and escape. In the first phase, elimination, transformed cells are recognized and destroyed by immune system. The rare tumor cells that are not destroyed in this phase may then enter the equilibrium phase, where their growth is prevented by immunity mechanisms. The escape phase represents the final phase of this process, where cancer cells begin to grow unconstrained by the immune system. In this study, we describe and analyze an evolutionary game theoretical model of proliferating, quiescent, and immune cells interactions for the first time. The proposed model is evaluated with constant and dynamic approaches. Population dynamics and interactions between the immune system and cancer cells are investigated. Stability of equilibria or critical points are analyzed by applying algebraic analysis. This model allows us to understand the process of cancer development and might help us design better treatment strategies to account for immunoediting. [ABSTRACT FROM AUTHOR]

Cancer cells and stromal cells often exchange growth factors with paracrine effects that promote cell growth: a form of cooperation that can be studied by evolutionary game theory. Previous models have assumed that interactions between cells are pairwise or that the benefit of a growth factor is a linear function of its concentration. Diffusible factors, however, affect multiple cells and generally have nonlinear effects, and these differences are known to have important consequences for evolutionary dynamics. Here, we study tumor–stroma paracrine signaling using a model with multiplayer collective interactions in which growth factors have nonlinear effects. We use multiple myeloma as an example, modelling interactions between malignant plasma cells, osteoblasts, and osteoclasts. Nonlinear benefits can lead to results not observed in linear models, including internal mixed stable equilibria and cyclical dynamics. Models with linear effects, therefore, do not lead to a meaningful characterization of the dynamics of tumor–stroma interactions. To understand the dynamics and the effect of therapies it is necessary to estimate the shape of the benefit functions experimentally and parametrize models based on these functions. [ABSTRACT FROM AUTHOR]

Nowadays, social networks are widely used as fast and ubiquitous media for sharing information. Rumor as unverified information also considerably spreads in social networks. The study of how rumor spreads and how it can be controlled, plays an important role in reducing social and psychological damages of rumor in social networks. Although recent researches have mainly focused on epidemic models and structure of social networks, they ignore the impact of people's decision on rumor process. In this paper, an evolutionary game model is proposed to analyze the rumor process in social network considering the impacts of people's decisions on rumor propagation and control. The model considers a rumor control mechanism via sending anti-rumor messages through rumor control centers. Factors affecting the people's decisions including social anxiety, people's attitude toward rumor/anti-rumor, strength of rumor/anti-rumor, influence of rumor control centers, and participation of people in discussions are studied in the model. The proposed game model is analyzed by replicator dynamics equations and simulation of the imitation update rule on a synthetic (Barabasi–Albert) and two real-world graphs of Twitter and Facebook. We further analyze the model in various environments considering people characteristics and society situation. Also we use a real rumor dataset of Twitter (Pheme dataset) to first compare the trends of people strategies (rumor/anti-rumor spreader and ignorant) derived by the model with the real trends of the traits of people in the rumor spreading on Twitter. Then we conduct a number of sensitivity analysis experiments to show the impact of different factors on rumor process. In fact, we analyze the trends of people strategies in Pheme dataset assuming various possible conditions. The analysis show that propagation of convincing anti-rumor messages and locating rumor control centers impact debunking the rumor. Moreover, it is shown that people attitude toward rumor/anti-rumor has significant impact on rumor spreading. Besides, factors such as social anxiety and strength of rumor accelerates rumor propagation. • A new evolutionary game model on graph for rumor propagation by considering the people characteristics and the society situations. • Anxiety of the society, people attitude toward rumor, and strength of rumor reinforce the rumor propagation. • People attitude toward anti-rumor, strength of anti-rumor, and querying the rumor control center can control rumor propagation. [ABSTRACT FROM AUTHOR]

Background and Objective Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection. Methods We propose a novel structure-based game-theoretic model for the evolution of HIV-1-Infected CD4+T-cells and invasion of the immune system. We discuss the theoretical basis of the stable equilibrium states of the evolutionary dynamics of four T-cells types as well as its significant results to understand and control HIV infection. The results include the importance of genetic variations and the process of establishing evolutionary dynamics of the virus quasispecies. Results Our results show that there is a direct dependency between some parameters such as mutation rates and the stability of equilibrium states in the HIV infection. This is an interesting result because these parameters can be changed by some pharmacotherapies and alternative treatments. Our model indicates that in an appropriate treatment the relative frequency of the wild type of virus quasispecies can be decreased in the population. Consequently, this can cause delaying the emergence of the AIDS phase. To assess the model, we investigate two new treatments for HIV. The results show that our model can predict the treatment results. Conclusions The paper shows that a structured-based evolutionary game theory can model the evolutionary dynamics of the infected T-cells and virus quasispecies. The model predicts certain aspects of the HIV infection process under several treatments. [ABSTRACT FROM AUTHOR]

The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users’ privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need of getting access to any information about each other. In the proposed protocol, each request will be sent with a randomly chosen ticket. The encrypted response produced by the server is sent to a particular mobile node (called broker node) over the network, based on the hash value of this ticket. The user will query the broker to get the response. All parts of the messages are encrypted except the fields required for the anonymizer and the broker. This will secure the packet exchange over the P2P network. The proposed protocol was implemented and tested successfully, and the experimental results showed that it could be deployed efficiently to achieve user privacy in location-based services. [ABSTRACT FROM PUBLISHER]

Remote Operating System (OS) Fingerprinting is a precursory step for launching attacks on the Internet. As a precaution against potential attacks, a remote machine can take a proactive counter-strategy to deceive fingerprinters. This is done by normalizing or mystifying the distinguishing behaviors in the packets. However, the unified modification causes a significant performance degradation to benign clients. Using a game-theoretic approach, we propose a selective and dynamic mechanism for counter-fingerprinting. We analyze the interaction between a fingerprinter and a target by modeling the problem as a signaling game and name it Defense-Game. Here, the defender observes the attacker's actions and takes moves accordingly. We derive the Nash equilibrium strategy profiles based on the information gain analysis and propose a defense mechanism named Strategic-Defender. Our game-theoretic approach appropriately distinguishes a fingerprinter from a benign client and mystifies packets to confuse the fingerprinter, while minimizing the side effects on benign clients. The performance analysis shows that the defense mechanism can reduce the probability of success of the fingerprinter significantly, without deteriorating the overall performance of other clients. Later, we restructure the game as a screening game where the attacker plays as the follower so that it can recognize the type of the target and act accordingly. We name the game Attack-Game and solve it for the equilibrium strategies. We evaluate the performance of Strategic-Defender when the attacker takes actions according to the optimal results of Attack-Game. The evaluation results show that the attacker's payoff may increase a bit, however, it is still too low to succeed. [ABSTRACT FROM AUTHOR]

Summary: Ephemeral social vehicular networks allow for short‐lived communications between occupants. While such transient interactions may provide important usage benefits such as traffic warnings, the reality of short‐lived interactions also poses challenges for deciding to participate in such social networks. In this paper, we develop a game‐theoretic model to understand the role of occupants' incentives to contribute to these ephemeral social networks. We rigorously study the properties of the model and resulting equilibrium outcomes. A further key objective of our work is to evaluate the robustness of our model to different notions of intrinsic motivation for network participation. As a baseline case, we assume that users are homogeneous regarding their motivation. Next, we investigate the impact of different motivational distributions and explore their impact on users' behaviors in a comprehensive simulation setup. We identify the circumstances under which ephemeral social networks are viable and can successfully sustain an increased level of network participation. [ABSTRACT FROM AUTHOR]