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Title: Effcient Implementation and Computational Analysis of Privacy-Preserving Protocols for Securing the Financial Markets.
Creator: Alvarez, Ramiro, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Auctions are a key economic mechanism for establishing the value of goods that have an uncertain price. In recent years, as a consequence of the ubiquitous emergence of technology, auctions can reach consumers, and as a result drive market prices, on a global scale. Collection of private information such as past trades exposes more information than desired by market participants. The leaked information can be statistically analyzed to provide auctioneers, or competitors, an advantage on...
Show moreAuctions are a key economic mechanism for establishing the value of goods that have an uncertain price. In recent years, as a consequence of the ubiquitous emergence of technology, auctions can reach consumers, and as a result drive market prices, on a global scale. Collection of private information such as past trades exposes more information than desired by market participants. The leaked information can be statistically analyzed to provide auctioneers, or competitors, an advantage on future transactions. The need to preserve privacy has become a critical concern to reach an accepted level of fairness, and to provide market participants an environment in which they can bid a true valuation. This research is about possible mechanisms to carry out sealed-bid auctions in a distributed setting, and provides the reader with the challenges that currently persevere in the field. The first chapter offers an introduction to different kinds of auction, and to describe sealed-bid auction. The next chapter surveys the literature, and provides necessary theoretical background. Moving on to chapter 3, instead of solely focusing on theoretical aspects of sealed-bid auctions, this chapter dives into implementation details, and demonstrates through communication and computational analysis how different settings affect performance.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013051
Subject Headings: Auctions., Financial markets., Tender offers (Securities).
Format: Document (PDF)

Title: New Structured Data Collection Approach for Real-Time Trust Measurement In Human-Autonomous Vehicle Interactions.
Creator: Shahrdar, Shervin, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Most of recent studies indicate that people are negatively predisposed toward utilizing autonomous systems. These findings highlight the necessity of conducting research to better understand the evolution of trust between humans and growing autonomous technologies such as self-driving cars (SDC). This research therefore presents a new approach for real-time trust measurement between passengers and SDCs. We utilized a new structured data collection approach along with a virtual reality (VR)...
Show moreMost of recent studies indicate that people are negatively predisposed toward utilizing autonomous systems. These findings highlight the necessity of conducting research to better understand the evolution of trust between humans and growing autonomous technologies such as self-driving cars (SDC). This research therefore presents a new approach for real-time trust measurement between passengers and SDCs. We utilized a new structured data collection approach along with a virtual reality (VR) SDC simulator to understand how various autonomous driving scenarios can increase or decrease human trust and how trust can be re-built in the case of incidental failures. To verify our methodology, we designed and conducted an empirical experiment on 50 human subjects. The results of this experiment indicated that most subjects could rebuild trust during a reasonable timeframe after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural background and past trust-related experiences of the subjects affect how they lose or regain their trust in SDCs. Our analysis showed that this model is highly effective for collecting real-time data from human subjects and lays the foundation for more-involved future research in the domain of human trust and autonomous driving.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013033
Subject Headings: Autonomous vehicles, Trust, Measurement
Format: Document (PDF)

Title: Utilizing a Game Theoretical Approach to Prevent Collusion and Incentivize Cooperation in Cybersecurity Contexts.
Creator: Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the...
Show moreIn this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the context of Blockchain and mining, we illustrate that by using the same socio-rational model, miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We therefore show that honest mining strategies become Nash Equilibrium in our setting. This thesis is laid out in the following manner. In chapter 2 an introduction to game theory is provided followed by a survey of previous works in game theoretic network security, in chapter 3 a new reputation-based model is introduced to be used within the context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution concept is introduced to force cooperation by each mining entity in Blockchain, and finally, in chapter 5, the concluding remarks and future works are presented.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00005950
Subject Headings: Dissertations, Academic -- Florida Atlantic University, Software-defined networks (Computer network technology), Blockchain, Cybersecurity
Format: Document (PDF)

Title: Using Electroencephalography and Structured Data Collection Techniques to Measure Passenger Emotional Response in Human-Autonomous Vehicle Interactions.
Creator: Park, Corey, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Wide spread consumer adoption of self-driving cars (SDC) is predicated on a level of trust between humans and the autonomous vehicle. Despite advances being made in the technical abilities of SDCs, recent studies indicate that people are negatively predisposed toward utilizing autonomous vehicles. To bridge the gap between consumer skepticism and adoption of SDCs, research is needed to better understand the evolution of trust between humans and growing autonomous technologies. The question of...
Show moreWide spread consumer adoption of self-driving cars (SDC) is predicated on a level of trust between humans and the autonomous vehicle. Despite advances being made in the technical abilities of SDCs, recent studies indicate that people are negatively predisposed toward utilizing autonomous vehicles. To bridge the gap between consumer skepticism and adoption of SDCs, research is needed to better understand the evolution of trust between humans and growing autonomous technologies. The question of mainstream acceptance and requisite trust is explored through integration of virtual reality SDC simulator, an electroencephalographic (EEG) recorder, and a new approach for real-time trust measurement between passengers and SDCs. An experiment on fifty human subjects was conducted where participants were exposed to scenarios designed to induce positive and negative trust responses. Emotional state was quantified by the EEG beta wave to alpha wave power ratio, and participants self-reported their levels of trust in the SDC after each segment.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013169
Subject Headings: Autonomous vehicles--Psychological aspects, Self-driving cars, Electroencephalography
Format: Document (PDF)

Title: IMPLEMENTATION AND ASSESSMENT OF THE REPUTATION-BASED MINING PARADIGM BY A COMPREHENSIVE SIMULATION.
Creator: Pourtahmasbi, Pouya, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Since the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated...
Show moreSince the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated solely based on game theoretical analyses and the real-world implications of this model are not known due to the lack of empirical data. To shed light on this issue, we designed a simulated mining platform to examine the effectiveness of the reputation-based mining paradigm through data analysis. We implemented block withholding attacks in our simulation and ran the following three scenarios: Reputation mode, non-reputation mode, and no attack mode. By comparing the results from these three scenarios, interestingly we found that the reputation-based mining paradigm decreases the number of block withholding attacks, and as a result, the actual revenue of individual miners becomes closer to their theoretical expected revenue. In addition, we observed that the confidence interval test can effectively detect block withholding attacks however, the test also results in a small number of false positive cases. Since the effectiveness of the reputation-based model relies on attack detection, further research is needed to investigate the effect of this model on other dishonest mining strategies.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013730
Subject Headings: Cryptocurrencies, Bitcoin, Blockchains (Databases)
Format: Document (PDF)

Title: IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator: Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding...
Show moreThe field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014029
Subject Headings: Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format: Document (PDF)

Title: DECENTRALIZED SYSTEMS FOR INFORMATION SHARING IN DYNAMIC ENVIRONMENT USING LOCALIZED CONSENSUS.
Creator: Zamir, Linir, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Achieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. Proof-of-Work is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized...
Show moreAchieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. Proof-of-Work is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized systems, also called blockchain systems, have been implemented in many applications such as supply chains, medical industry, and authentication. However, it is mostly used as a cryptocurrency foundation for token exchange. For these systems to operate properly, they are required to be robust, scalable, and secure. This dissertation provides a different approach of using consensus algorithms for allowing information sharing among nodes in a secured fashion while maintaining the security and immutability of the consensus algorithm. The consensus algorithm proposed in this dissertation utilizes a trust parameter to enforce cooperation, i.e., a trust value is assigned to each node and it is monitored to prevent malicious activities over time. This dissertation also proposes a new solution, named localized consensus, as a method that allows nodes in small groups to achieve consensus on information that is only relevant to that small group of nodes, thus reducing the bandwidth of the system. The proposed models can be practical solutions for immense and highly dynamic environments with validation through trust and reputation values. Application for such localized consensus can be communication among autonomous vehicles where traffic data is relevant to only a small group of vehicles and not the entirety of the system.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014028
Subject Headings: Blockchain, Consensus algorithms
Format: Document (PDF)

Title: ANALYSIS OF DRIVING BEHAVIORS AND RELEVANT DRIVING PREFERENCES REGARDING SELF-DRIVING CARS.
Creator: Tolbert, Steven William, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This thesis explores the cross-cultural demands from self-driving cars in regards to their trust, safety, and driving styles. Through the use of international survey data we establish several AI trust and behavior metrics that can be used for understanding cross-cultural expectations from self-driving cars that can potentially address problems of trust between passengers and self-driving cars, social acceptability of self-driving cars, and development of customized autonomous driving...
Show moreThis thesis explores the cross-cultural demands from self-driving cars in regards to their trust, safety, and driving styles. Through the use of international survey data we establish several AI trust and behavior metrics that can be used for understanding cross-cultural expectations from self-driving cars that can potentially address problems of trust between passengers and self-driving cars, social acceptability of self-driving cars, and development of customized autonomous driving technologies. Further this thesis provides a serverless data-collection framework for future research in driving behaviors.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014115
Subject Headings: Automated vehicles, Automated vehicles--Social aspects, Artificial intelligence, Human-machine systems
Format: Document (PDF)

Title: STUDY AND ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR DETECTION OF DISTRACTED DRIVERS.
Creator: Qu, Fangming, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The rise of Advanced Driver-Assistance Systems (ADAS) and Autonomous Vehicles (AVs) emphasizes the urgent need to combat distracted driving. This study introduces a fresh approach for improved detection of distracted drivers, combining a pre-trained Convolutional Neural Network (CNN) with a Bidirectional Long Short- Term Memory (BiLSTM) network. Our analysis utilizes both spatial and temporal features to examine a broad array of driver distractions. We demonstrate the advantage of this CNN...
Show moreThe rise of Advanced Driver-Assistance Systems (ADAS) and Autonomous Vehicles (AVs) emphasizes the urgent need to combat distracted driving. This study introduces a fresh approach for improved detection of distracted drivers, combining a pre-trained Convolutional Neural Network (CNN) with a Bidirectional Long Short- Term Memory (BiLSTM) network. Our analysis utilizes both spatial and temporal features to examine a broad array of driver distractions. We demonstrate the advantage of this CNN-BiLSTM framework over conventional methods, achieving significant precision (up to 98.97%) on the combined ’Union Dataset,’ merging the Kaggle State Farm Dataset and AUC Distracted Driver Dataset (AUC-DDD). This research enhances safety in autonomous vehicles by providing a solid and flexible solution for everyday use. Our results mark considerable progress in accurately identifying driver distractions, pushing the boundaries of safety technology in AVs.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014418
Subject Headings: Deep learning (Machine learning), Distracted driving, Transportation--Safety measures, Automated vehicles--Safety measures
Format: Document (PDF)

Title: A Collaborative Approach for Real-Time Measurements of Human Trust, Satisfaction and Frustration in Human-Robot Teaming.
Creator: Gonzalez Moya, Iker Javier, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis aims at real-time measurements of human trust, satisfaction, and frustration in human-robot teaming. Recent studies suggest that humans are inclined to have a negative attitude towards using autonomous systems. These ndings elevate the necessity of conducting research to better understand the key factors that a ect the levels of trust, satisfaction and frustration in Human-Robot Interaction (HRI). We utilized a new sequential and collaborative approach for HRI data collection that...
Show moreThis thesis aims at real-time measurements of human trust, satisfaction, and frustration in human-robot teaming. Recent studies suggest that humans are inclined to have a negative attitude towards using autonomous systems. These ndings elevate the necessity of conducting research to better understand the key factors that a ect the levels of trust, satisfaction and frustration in Human-Robot Interaction (HRI). We utilized a new sequential and collaborative approach for HRI data collection that employed trust, satisfaction and frustration as primarily evaluative metrics. We also used haptic feedback through a soft actuator armband to help our human subjects control a robotic hand for grabbing or not grabbing an object during our interaction scenarios. Three experimental studies were conducted during our research of which the rst was related to the evaluation of aforementioned metrics through a collabora- tive approach between the Baxter robot and human subjects. The second experiment embodied the evaluation of a newly fabricated 3D- nger for the I-Limb robotic hand through a nuclear-waste glove. The third experiment was based on the two previous studies that focused on real-time measurements of trust, satisfaction and frustration in human-robot teaming with the addition of pressure feedback to the system through soft actuators. In the last case, human subjects had more controls over our robotic systems compared to earlier experiments leading to a more collaborative interaction and teaming. The results of these experiments illustrated that human subjects can rebuild their trust and also increase their satisfaction levels while lowering their frus- tration levels after failures or any faulty behavior. Furthermore, our analyses showed that our methods are highly e ective for collecting honest and genuine data from hu- man subjects and lays the foundation for more-involved future research in the domain of human-robot teaming.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013064
Subject Headings: Human-robot interaction., Haptic devices.
Format: Document (PDF)

Title: SELECTED APPLICATIONS OF MPC.
Creator: Ghaseminejad, Mohammad Raeini, Liu, Feng-Hao, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Secure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in...
Show moreSecure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in privacy-preserving data mining and machine learning, secure signal processing, secure genome analysis, sealed-bid auctions, etc. There are different approaches for realizing secure MPC. Some commonly used approaches include secret sharing schemes, Yao's garbled circuits, and homomorphic encryption techniques. The main focus of this dissertation is to further investigate secure multiparty computation as an appealing area of research and to study its applications in different domains. We specifically focus on secure multiparty computation based on secret sharing and fully homomorphic encryption (FHE) schemes. We review the important theoretical foundations of these approaches and provide some novel applications for each of them. For the fully homomorphic encryption (FHE) part, we mainly focus on FHE schemes based on the LWE problem [142] or RLWE problem [109]. Particularly, we provide a C++ implementation for the ring variant of a third generation FHE scheme called the approximate eigenvector method (a.k.a., the GSW scheme) [67]. We then propose some novel approaches for homomorphic evaluation of common functionalities based on the implemented (R)LWE [142] and [109] and RGSW [38,58] schemes. We specifically present some constructions for homomorphic computation of pseudorandom functions (PRFs). For secure computation based on secret sharing [150], we provide some novel protocols for secure trust evaluation (STE). Our proposed STE techniques [137] enable the parties in trust and reputation systems (TRS) to securely assess their trust values in each other while they keep their input trust values private. It is worth mentioning that trust and reputation are social mechanisms which can be considered as soft security measures that complement hard security measures (e.g., cryptographic and secure multiparty computation techniques) [138, 171].
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014018
Subject Headings: Data encryption (Computer science), Computers, privacy and data protection, Computer security
Format: Document (PDF)

Blockchain (2)	+ -
Deep learning (Machine learning) (2)	+ -
Aesthetics (1)	+ -
Artificial intelligence (1)	+ -
Auctions. (1)	+ -

Automated vehicles (1)	+ -
Automated vehicles--Safety measures (1)	+ -
Automated vehicles--Social aspects (1)	+ -
Autonomous vehicles (1)	+ -
Autonomous vehicles--Psychological aspects (1)	+ -
Bitcoin (1)	+ -
Blockchains (Databases) (1)	+ -
Computer security (1)	+ -
Computer vision (1)	+ -
Computers, privacy and data protection (1)	+ -
Consensus algorithms (1)	+ -
Cryptocurrencies (1)	+ -
Cybersecurity (1)	+ -
Data encryption (Computer science) (1)	+ -
Dissertations, Academic -- Florida Atlantic University (1)	+ -
Distracted driving (1)	+ -
Electroencephalography (1)	+ -
Financial markets. (1)	+ -
Haptic devices. (1)	+ -
Human-machine systems (1)	+ -
Human-robot interaction. (1)	+ -
Image Quality (1)	+ -
Measurement (1)	+ -
Self-driving cars (1)	+ -
Software-defined networks (Computer network technology) (1)	+ -

College of Engineering and Computer Science (11)	+ -
Department of Computer and Electrical Engineering and Computer Science (11)	+ -
Florida Atlantic University (11)	+ -
Nojoumian, Mehrdad (11)	+ -
Golchubian, Arash (2)	+ -

Alvarez, Ramiro (1)	+ -
Ghaseminejad, Mohammad Raeini (1)	+ -
Gonzalez Moya, Iker Javier (1)	+ -
Liu, Feng-Hao (1)	+ -
Park, Corey (1)	+ -
Pourtahmasbi, Pouya (1)	+ -
Qu, Fangming (1)	+ -
Shahrdar, Shervin (1)	+ -
Tolbert, Steven William (1)	+ -
Zamir, Linir (1)	+ -

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