Gaby G. Dagher

Due to HIPAA and other privacy regulations, it is imperative to maintain patient privacy while conducting research on patient health records. In this paper, we propose AegisBlock, a patient-centric access controlled framework to share medical records with researchers such that the anonymity of the patient is maintained while ensuring the trustworthiness of the data provided to researchers. AegisBlock allows for patients to provide access to their medical data, verified by miners. A researcher submits a time-based range query to request access to records from a certain patient, and upon patient approval, access will be granted. Our experimental evaluation results show that AegisBlock is scalable with respect to the number of patients and hospitals in the system, and efficient with up to 50 % of malicious miners.

Due to HIPAA and other privacy regulations, it is imperative to maintain patient privacy while conducting research on patient health records. In this paper, we propose AegisBlock, a patient-centric access controlled framework to share medical records with researchers such that the anonymity of the patient is maintained while ensuring the trustworthiness of the data provided to researchers. AegisBlock allows for patients to provide access to their medical data, verified by miners. A researcher submits a time-based range query to request access to records from a certain patient, and upon patient approval, access will be granted. Our experimental evaluation results show that AegisBlock is scalable with respect to the number of patients and hospitals in the system, and efficient with up to 50% of malicious miners.

Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.

In the contemporary digital landscape, cybersecurity has become a critical issue due to the increasing frequency and sophistication of cyber attacks. This study utilizes a non-zero-sum game theoretical framework to model the strategic interactions between cyber attackers and defenders, with the objective of identifying optimal strategies for both. By defining precise payoff functions that incorporate the probabilities and costs associated with various exploits, as well as the values of network nodes and the costs of deploying honeypots, we derive Nash equilibria that inform strategic decisions. The proposed model is validated through extensive simulations, demonstrating its effectiveness in enhancing network security. Our results indicate that high-probability, low-cost exploits like Phishing and Social Engineering are more likely to be used by attackers, necessitating prioritized defense mechanisms. Our findings also show that increasing the number of network nodes dilutes the attacker's efforts, thereby improving the defender's payoff. This study provides valuable insights into optimizing resource allocation for cybersecurity and highlights the scalability and practical applicability of the game-theoretic approach.

Cybersecurity is an interdisciplinary field that is concerned with protecting digital assets from cyber-attacks aiming to illegally access sensitive information in order to tamper and disrupt systems and processes. Producing cybersecurity materials that are vertically-aligned is highly desired, given the shortage of cybersecurity educators and the dynamic and evolving nature of cybersecurity. More specifically, universities must do more to help fill the huge cybersecurity workforce shortage and address the lack of materials centered around adversarial thinking. In this paper, we propose a four-step process to turn a recent cybersecurity paper into a hands-on lab that utilizes game theory to promote adversarial thinking and show a case study where this process was used. The four-step process explains how papers are chosen, their research replicated, the production of lab materials, and complementary materials for students to work from. The case study demonstrates this process in practice and explains how game theory is incorporated into the lab.

Blockchain is an emerging distributed and decentralized technology that promises to revolutionize the healthcare sector by securely storing and maintaining incorruptible electronic health record data. Consensus protocols are at the core of blockchain technology. They establish security and integrity in the system by ensuring that the majority of miners are in agreement on all transactions and blocks added to the distributed ledger. While many consensus protocols have been proposed, most of them require heavy computation and are not scalable. In this work, we propose a novel permissioned consensus protocol, named ACCORD, a multi-leader (quorum-based) protocol that achieves fork-resistance, robustness, and scalability. To achieve this, ACCORD consists of three distinct components: 1) an asynchronous quorum selection procedure to designate the creators of future blocks; 2) a block creation protocol run by the quorum to prevent omissions in the presence of honest quorum members; and 3) a decentralized arbitration protocol to ensure consensus by voting. Additionally, we implemented the protocol and conducted experiments to demonstrate scalability, robustness, and fairness.

Achieving interoperability between healthcare providers is a major challenge. Current systems for managing prescription records suffer from data siloing, unnecessary record duplication, and slow record transfers. In many systems, patients do not retain control over their prescription data. Instead, they must use an intermediary to access or transfer their records. Furthermore, record transfers suffer from differing standards between providers, outdated communication methods, and information blocking. Solving these problems necessitates the creation of an interoperable prescription management system. Realizing such a system requires considering security, efficiency, scalability, and other challenges. Recent regulatory actions attempt to address these challenges, but fundamental issues persist. This paper proposes a patient-centric and interoperable prescription system that ensures patient control, prevents information blocking, and improves transfer efficiency. We call our solution VigilRx—a system that uses blockchain and smart contracts to manage prescriptions. Stakeholders exist as one of three role-based smart contracts within the system: patient, prescriber, or pharmacy. These role contracts ensure the system is patient-centric by assigning ownership of prescription records directly to patients. Our smart contracts also ensure the system’s interoperability, as we use a standardized prescription contract to ensure records can be easily managed. VigilRx’s use of blockchain also promotes transparency by providing patients an explicit list of parties that hold permission to access their records. Use of existing software patterns allows the system to adapt as needed. We implement VigilRx and show that it is both scalable and efficient.

One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying, given that online records typically live on the Internet for quite a long time and are hard to control. In this article, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network (SN). We analyze tweets to determine their relation to cyberbullying while considering the context in which the tweets exist in order to optimize their bullying score. We also propose a centrality measure to detect cyberbullies from a cyberbullying SN and show that it outperforms other existing measures. We experiment on a data set of 5.6 million tweets, and our results show that the proposed approach can detect cyberbullies with high accuracy while being scalable with respect to the number of tweets.