Saudi Cultural Missions Theses & Dissertations
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Item Restricted Adapting Homes in Saudi Arabia to Accommodate International Tourists: A Socio-cultural Design Study in Riyadh(The University of Sheffield, 2024) Almusaylihi, Eman; Lanuza, Felipe.9 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.13 0Item Restricted Deep Reinforcement Learning and Privacy Preserving with Differential Privacy(Saudi Digital Library, 2023-09-15) Abahussein, Suleiman; Zhu, TianqingWith the rapid advances in technology in the current era and the emergence of multiple technologies that have transformed society, Deep reinforcement learning (DRL) offers promising solutions and enhanced capabilities with demonstrated superior results. Deep reinforcement learning is a subfield of artificial intelligence that has attracted significant research attention and development over the past few years. Reinforcement learning (RL) is enabled by deep learning to address the intractable problems previously encountered, for example, how an agent learns to play video games with only pixels as input. In the field of robotics, deep reinforcement learning algorithms are employed where control policies for robots can be learned directly from camera inputs in the real world. Deep RL aims to maximize the cumulative reward through the process of trial and error to find the optimal policy. The learning method is carried out by executing the action, receiving corresponding rewards and then moving to the next state. In some complex problems, it is necessary to have more than one RL agent, which leads to the idea of multi-agent reinforcement learning, where more than one agent works together and shares the same environment to achieve a certain goal. An example of multi-agent RL is multiple robotics working to rescue an individual. Nowadays, deep reinforcement learning is used in various areas, such as recommendation systems, robotics, and health applications. While there are enormous benefits to using these technologies, there are also significant privacy concerns associated with them. The learning process in deep reinforcement learning involves performing the action, receiving the reward, and moving to the next state. Deep reinforcement learning is vulnerable to adversary attacks, and private information can be inferred by an adversary using recursive querying. The trained policy could be released to the client side, which could enable the adversary to infer private information from the trained policy, pose a real risk, and constitute a breach of privacy. This research focuses on deep reinforcement learning and multi-agent reinforcement learning and the related privacy issues. The contributions made by this research are as follows: • This research proposes a solution for online food delivery services to increase the number of food delivery orders and thereby increase the long-term income of couriers. The solution involves leveraging multi-agent reinforcement learning by employing two multi-agent reinforcement learning algorithms to guide couriers to areas with a high demand for food delivery orders. • This research proposes a solution to protect privacy in Double and Dueling Deep Q Networks by adopting the Differentially Private Stochastic Gradient Descent (DPSGD) method and injecting Gaussian noise into the gradient. • This research proposes the Protect User Location Method (PULM) to protect customer location information in online food delivery services. This method injects differential privacy Laplace noise based on two factors: the size of the city and the frequency of customer orders. • This research proposes a Protect Trajectory and Location in Food Delivery (PTLFD) method to maintain the privacy of the customer’s stored data in online food delivery services. This method leverages multi-agent reinforcement learning and differential privacy to protect customer location information.27 0Item Restricted IoT: current challenges and future applications(Saudi Digital Library, 2023-08-30) Almutairi, Nader; Palego, CristianoThis master's thesis explores the dynamic landscape of Internet of Things (IoT) technology, focusing on its transformative potential and obstacles. The study explores the role of IoT devices in reshaping industries, enhancing efficiency, and enhancing resource management by navigating the complex web of IoT devices. The investigation identifies key contributors to the prevalent security vulnerabilities in IoT systems and suggests strategies to strengthen their defences. The research creates a comprehensive framework that combines encryption, authentication, and anomaly detection mechanisms to address the pressing need for secure and efficient IoT solutions. Utilising cutting-edge technologies such as machine learning and blockchain, the framework not only improves the security of IoT devices, but also guarantees data integrity and user privacy. This solution's significance rests in its capacity to pave the way for safer and more reliable IoT technology, thereby nurturing confidence among users, industries, and policymakers. The results of the study demonstrate the effectiveness of the proposed framework in protecting IoT ecosystems from cyber threats, while also addressing ethical and regulatory concerns. Beyond technology, this research has implications for societal well-being, sustainable practises, and economic growth. By mitigating the security risks associated with the Internet of Things, this study establishes the foundation for realising the maximum potential of IoT technology in various industries.42 0Item Restricted Developing an Efficient and Privacy-Preserving Energy Theft Detection System for Smart Grids(Saudi Digital Library, 2023-09-25) Alromih, Arwa; Clark, John; Gope, ProsantaEnergy plays an essential role in our lives. Merging the existing electricity networks with distributed energy resources and information and communications technology (ICT) changes how companies and customers generate, distribute, and consume energy. This integration transforms the legacy electricity networks into smart systems, or what is currently known as the Smart Grid (SG). Smart grid infrastructure has been one of the major industrial revolutions that has attracted widespread adoption across the globe. Therefore, they can be the target of major security risks as they are not inherently secure. In this sector, sensors’ and meters’ data are the main factors in any decision-making process. This introduces the need to develop appropriate security mechanisms that ensure data integrity. One of the main attacks against data integrity in energy networks is energy theft. This attack can be made by injecting false consumption data into the network. The consequences of a successful energy theft attack on smart grid systems can be severe and far-reaching as it can result in power outages and physical damage to equipment which can be a safety hazard to individuals. Therefore, secure techniques are needed to detect any anomalies or injection attempts and smart meter data integrity should be considered and ensured. In this thesis, we propose three machine learning (ML) based energy theft detectors that address the existing challenges facing current research in this domain. In particular, we consider the impact proposed by prosumers in launching new types of energy thefts and how to detect them. We also show how to fully utilise data from multiple sources for better detection performance. To decrease the probability of any privacy breaches caused by the use of customers’ data, privacy-preserving approaches are proposed. Lastly, we tackle the significant impact on demand management caused by energy thefts by proposing a combined energy theft detector with demand management. The findings presented in this thesis show that our approaches can accurately detect energy thefts, with minimal information leakage. Moreover, the results are also promising in providing a clear link between reliably managing demand when energy theft is considered.24 0Item Restricted Secure Data-Sharing Among Healthcare Organisations in Saudi Arabia(Saudi Digital Library, 2023-07-17) Alzahrani, Ahmed; Wills, GaryThe healthcare sector is suffering from the inefficiencies in handling its data. Many patients and healthcare organisations are frustrated by the numerous hurdles to obtaining current real-time patient information that are leading to delays in treatment. The healthcare sector’s attention has been drawn to blockchain technology for a part of the solution, especially after this technology was successfully applied in the financial sector to improve the security of transactions. The lack of data-sharing in the healthcare sector is considered a significant issue worldwide. This research focuses on the gap by investigating the benefits of using blockchain at the Ministry of Health in Saudi Arabia. The study achieves this by providing a detailed analysis of the healthcare sector and evaluating how blockchain technology improves data-sharing in a more secure way. This research proposes a framework that identifies the factors that will provide data-sharing among healthcare organisations using blockchain. The framework has three categories: healthcare systems factors; security factors; and blockchain factors. These were identified by critically reviewing published studies together with factors from the relevant industrial standards within the context of the Kingdom of Saudi Arabia (KSA). A triangulation technique was used to achieve reliable results in three steps: a literature review; expert review; and questionnaires. This provided a comprehensive picture of the research topic, validating and confirming the results. To construct the framework the factors of the framework were comprehensively studied and extracted from the literature, then analysed, cleared of duplicates and categorised. Once the framework had been developed, to review and confirm it a study was carried out with healthcare IT specialists and blockchain experts. The expert review findings confirmed that all the proposed factors were important, and suggested recategorising one factor and removing another. After revising the proposed framework according to the expert review and recommendations, a questionnaire was distributed to healthcare IT specialists and blockchain experts in various organisations. Its results were analysed via a one-sample t-test and its data integrity analysed using Cronbach’s alpha, showing that all the factors are statistically significant. The confirmed framework has been based on literature and expert reviews and is supported by a practitioner survey. The framework can be used to inform decision-makers and the Ministry of Health about the factors that will provide data-sharing among healthcare organisations using blockchain. A new instrument was developed. A total of 238 IT and blockchain experts in Saudi healthcare organisations used the instrument. It was developed using the framework to identify the factors that will provide data-sharing among healthcare organisations. The instrument was evaluated using two tests that examined the internal reliability and the validity tests. The results from the instrument were used to develop a model using Structural Equation Modelling (SEM). The resulting data clearly showed a good fit of the structural model and measurement analyses. The key outcomes of the validation study revealed that the factors were discovered to have a direct and statistically significant effect on the model. This specifies that the proposed model fits the data and applies to the KSA context. The contributions of this research are as follows: first, it developed a framework within the KSA context and, second, from the framework a data-sharing instrument was developed, the results of which were used to generate a structural equation model. Overall, the outcomes of this study are valuable information in terms of recommendations to experts and healthcare organisations. Simply put, these findings can assist data sharing and encourage the spread of this phenomenon across countries in the Middle East, particularly in Saudi Arabia.47 0