Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Privacy-Preserving Structure Learning for Geospatial Data Using Information-Theoretic Dependency Measures(Saudi Digital Library, 2025) Mudhish, Ahmed; Pradeep, ChowriappaThis dissertation proposes a privacy-preserving framework for structure learning in Bayesian networks (BNs) that addresses the challenges of distributed geospatial data face. Geospatial datasets often exhibit region-specific patterns such as sparsity and nonlinear dependencies. These patterns undermine the effectiveness of traditional machine learning models. Additionally, learned BN structures may reveal sensitive relationships in the generated graph by BNs. These relationships pose a significant privacy risk if reverse-engineered. To address these issues, three novel algorithms are introduced. First, the Selective Naïve Bayes with HSIC (SNB-HSIC) algorithm applies a kernel-based dependency measure to filter redundant and irrelevant features in sparse datasets, improving structure clarity without compromising classification accuracy. Second, the Controlled K-Dependence Bayesian Network (CKDBN) extends traditional K-dependence models by giving the option to select the optimal number of parents each node can have based on data-driven thresholds. THE CKDBN enables a flexible structure learning algorithm that can handle complex or high-dimensional settings. Third, the BNVeil algorithm introduces a privacy-preserving method that can obfuscate highly connected nodes using Laplace noise to protect the model’s logic from adversarial inference. All the frameworks are validated on both the full and partitioned geospatial datasets via a series of experiments that evaluate the structure quality, the predictive performance, and the robustness of privacy-preserving concerns. The results of the experiments indicate that the proposed methods in this dissertation achieve better accuracy than traditional BN models and significantly enhance interpretability and structural privacy. The three algorithms offer a practical and secure solution for region-based geospatial data.18 0Item Restricted Sharper Swords, Tougher Shields The Impact of GenAI on the Offensive-Defensive Balance in Cyberspace(King’s College London, 2024-08-26) Abanumay, Sarah; Devanny, JosephThis dissertation investigates the relative advantages of generative artificial intelligence (GenAI) to cyber defensive and offensive operations. It examines how state and non-state actors can utilise GenAI, arguing that while GenAI can significantly enhance both offensive and defensive cyber operations, the extent of these benefits is determined by four interrelated factors: geostrategic priorities, economic resources, regulatory frameworks, and organisational capabilities. These factors collectively shape the cyber offensive-defensive balance, a central concept in this study for understanding GenAI's impact on cyber operations. The research follows a literature-based methodology guided by frameworks such as the NIST Cybersecurity Framework 2.0 and the Cyber Kill Chain. The dissertation is structured into three chapters: the evolution of GenAI in cybersecurity, an analysis of strategic debates and the offensive-defensive balance and an exploration of the factors shaping this balance. The findings provide valuable insights for maintaining cybersecurity in the GenAI era.24 0Item Restricted INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, AvielThe proliferation of interconnected smart devices, once ordinary household appliances, has led to an exponential increase in sensitive data collection and transmission. The security and privacy of IoT devices, however, have lagged behind their rapid deployment, creating vulnerabilities that can be exploited by malicious actors. While security attacks on IoT devices have garnered attention, privacy implications often go unnoticed, exposing users to potential risks without their awareness. Our research contributes to a deeper understanding of user privacy concerns and implications caused by data collection within the vast landscape of the Internet of Things (IoT). We uncover the true extent of data accessible to adversarial individuals and propose a solution to ensure data privacy in precarious situations. We provide valuable insights, paving the way for a more informed and comprehensive approach to studying, addressing, and raising awareness about privacy issues within the evolving landscape of smart home environments.31 0