1,218 research outputs found
International Conference on Computer Science and Communication Engineering
UBT Annual International Conference is the 8th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level.
This conference consists of sub conferences in different fields like:
– Computer Science and Communication Engineering– Management, Business and Economics– Mechatronics, System Engineering and Robotics– Energy Efficiency Engineering– Information Systems and Security– Architecture – Spatial Planning– Civil Engineering , Infrastructure and Environment– Law– Political Science– Journalism , Media and Communication– Food Science and Technology– Pharmaceutical and Natural Sciences– Design– Psychology– Education and Development– Fashion– Music– Art and Digital Media– Dentistry– Applied Medicine– Nursing
This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event.
Edmond Hajrizi, President of UBTUBT – Higher Education Institutio
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From Dataveillance to Data Economy: Firm View on Data Protection
The increasing availability of electronic records and the expanded reliance on online communications and services have made available a huge amount of data about people’s behaviours, characteristics, and preferences. Advancements in data processing technology, known as big data, offer opportunities to increase organisational efficiency and competitiveness. Analytically sophisticated companies excel in their ability to extract value from the analysis of digital data. However, in order to exploit the potential economic benefits produced by big data and analytics, issues of data privacy and information security need to be addressed. In Europe, organisations processing personal data are being required to implement basic data protection principles, which are considered difficult to implement in big data environments. Little is known in the privacy studies literature about how companies manage the trade-off between data usage and data protection. This study contributes to explore the corporate data privacy environment, by focusing on the interrelationship between the data protection legal regime, the application of big data analytics to achieve corporate objectives, and the creation of an organisational privacy culture. It also draws insights from surveillance studies, particularly the idea of dataveillance, to identify potential limitations of the current legal privacy regime. The findings from the analysis of survey data show that big data and data protection support each other, but also that some frictions can emerge around data collection and data fusion. The demand for the integration of different data sources poses challenges to the implementation of data protection principles. However, this study finds no evidence that data protection laws prevent data gathering. Implications relevant for the debate on the reform of European data protection law are also drawn from these findings
Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud
State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage
Analyzing the Effectiveness of Legal Regulations and Social Consequences for Securing Data
There is a wide range of concerns and challenges related to stored data security – which range from privacy and management to operations readiness, These challenges span from financial to personal and public impact. With an abundance of regulations for the enforcement of data security and emerging requirements proposed every year, organizations cannot avoid the legal or social implications of inadequate data protection. Today, public spotlight and awareness are challenging organizations to enhance how data is protected more than at any other time. For this reason, organizations have made significant efforts to improve security.
When looking at precautions or changes, the factors considered are costs associated with such action, a potential consequence of not acting, impact on users, the effort required, and the scope. For this reason, leaders need to make the hard decisions of which risks they can live with and which need to be reduced because it is unrealistic to think that data security can be guaranteed. However, it is essential to have physical, administrative, and technical controls to mitigate data risks. Data protection regulations define requirements, create procedures to identify the associated risks, determine the extent of the impact, and identify what precautions should be taken.
This dissertation defined seven areas for consideration related to stored data security. The research facilitated developing a measurement tool to gather and analyze the knowledge and opinions of working professionals within the United States. The study was performed from July to October 2020, which resulted in a quantitative data sample used to analyze the effectiveness of legal regulations and social consequences for securing data
Measures of Privacy Protection on Social Environments
Tesis por compendio[EN] Nowadays, online social networks (OSNs) have become a mainstream cultural phenomenon for millions of Internet users. Social networks are an ideal environment for
generating all kinds of social benefits for users. Users share experiences, keep in touch
with their family, friends and acquaintances, and earn economic benefits from the
power of their influence (which is translated into new job opportunities). However,
the use of social networks and the action of sharing information imply the loss of the
users’ privacy.
Recently, a great interest in protecting the privacy of users has emerged. This situation
has been due to documented cases of regrets in users’ actions, company scandals produced by misuse of personal information, and the biases introduced by privacy mechanisms. Social network providers have included improvements in their systems to reduce
users’ privacy risks; for example, restricting privacy policies by default, adding new privacy settings, and designing quick and easy shortcuts to configure user privacy settings.
In the privacy researcher area, new advances are proposed to improve privacy mechanisms, most of them focused on automation, fine-grained systems, and the usage of
features extracted from the user’s profile information and interactions to recommend
the best privacy policy for the user. Despite these advances, many studies have shown
that users’ concern for privacy does not match the decisions they ultimately make in
social networks. This misalignment in the users’ behavior might be due to the complexity of the privacy concept itself. This drawback causes users to disregard privacy risks,
or perceive them as temporarily distant. Another cause of users’ behavior misalignment might be due to the complexity of the privacy decision-making process. This is
because users should consider all possible scenarios and the factors involved (e.g., the
number of friends, the relationship type, the context of the information, etc.) to make
an appropriate privacy decision.
The main contributions of this thesis are the development of metrics to assess privacy
risks, and the proposal of explainable privacy mechanisms (using the developed metrics) to assist and raise awareness among users during the privacy decision process.
Based on the definition of the concept of privacy, the dimensions of information scope
and information sensitivity have been considered in this thesis to assess privacy risks.
For explainable privacy mechanisms, soft paternalism techniques and gamification elements that make use of the proposed metrics have been designed. These mechanisms
have been integrated into the social network PESEDIA and evaluated in experiments
with real users. PESEDIA is a social network developed in the framework of the Master’s
thesis of the Ph.D. student [15], this thesis, and the national projects “Privacy in Social Educational Environments during Childhood and Adolescence” (TIN2014-55206-
R) and “Intelligent Agents for Privacy Advice in Social Networks” (TIN2017-89156-R).
The findings confirm the validity of the proposed metrics for computing the users’ scope
and the sensitivity of social network publications. For the scope metric, the results also
showed the possibility of estimating it through local and social centrality metrics for
scenarios with limited information access. For the sensitivity metric, the results also
remarked the users’ misalignment for some information types and the consensus for a
majority of them. The usage of these metrics as part of messages about potential consequences of privacy policy choices and information sharing actions to users showed
positive effects on users’ behavior regarding privacy. Furthermore, the findings of exploring the users’ trade-off between costs and benefits during disclosure actions of personal information showed significant relationships with the usual social circles (family
members, friends, coworkers, and unknown users) and their properties. This allowed
designing better privacy mechanisms that appropriately restrict access to information and reduce regrets. Finally, gamification elements applied to social networks and
users’ privacy showed a positive effect on the users’ behavior towards privacy and safe
practices in social networks.[ES] En la actualidad, las redes sociales se han convertido en un fenĂłmeno cultural dominante para millones de usuarios de Internet. Las redes sociales son un entorno ideal
para la generaciĂłn de todo tipo de beneficios sociales para los usuarios. Los usuarios
comparten experiencias, mantienen el contacto con sus familiares, amigos y conocidos,
y obtienen beneficios econĂłmicos gracias al poder de su influencia (lo que se traduce en
nuevas oportunidades de trabajo). Sin embargo, el uso de las redes sociales y la acciĂłn
de compartir informaciĂłn implica la perdida de la privacidad de los usuarios.
Recientemente ha emergido un gran interés en proteger la privacidad de los usuarios. Esta situación se ha debido a los casos de arrepentimientos documentados en las
acciones de los usuarios, escándalos empresariales producidos por usos indebidos de
la informaciĂłn personal, y a los sesgos que introducen los mecanismos de privacidad.
Los proveedores de redes sociales han incluido mejoras en sus sistemas para reducir los
riesgos en privacidad de los usuarios; por ejemplo, restringiendo las polĂticas de privacidad por defecto, añadiendo nuevos elementos de configuraciĂłn de la privacidad, y
diseñando accesos fáciles y directos para configurar la privacidad de los usuarios. En el
campo de la investigaciĂłn de la privacidad, nuevos avances se proponen para mejorar
los mecanismos de privacidad la mayorĂa centrados en la automatizaciĂłn, selecciĂłn de
grano fino, y uso de caracterĂsticas extraĂdas de la informaciĂłn y sus interacciones para
recomendar la mejor polĂtica de privacidad para el usuario. A pesar de estos avances,
muchos estudios han demostrado que la preocupaciĂłn de los usuarios por la privacidad no se corresponde con las decisiones que finalmente toman en las redes sociales.
Este desajuste en el comportamiento de los usuarios podrĂa deberse a la complejidad
del propio concepto de privacidad. Este inconveniente hace que los usuarios ignoren
los riesgos de privacidad, o los perciban como temporalmente distantes. Otra causa
del desajuste en el comportamiento de los usuarios podrĂa deberse a la complejidad
del proceso de toma de decisiones sobre la privacidad. Esto se debe a que los usuarios
deben considerar todos los escenarios posibles y los factores involucrados (por ejemplo, el nĂşmero de amigos, el tipo de relaciĂłn, el contexto de la informaciĂłn, etc.) para
tomar una decisiĂłn apropiada sobre la privacidad.
Las principales contribuciones de esta tesis son el desarrollo de métricas para evaluar los riesgos de privacidad, y la propuesta de mecanismos de privacidad explicables
(haciendo uso de las métricas desarrolladas) para asistir y concienciar a los usuarios
durante el proceso de decisiĂłn sobre la privacidad. Atendiendo a la definiciĂłn del
concepto de la privacidad, las dimensiones del alcance de la información y la sensibilidad de la información se han considerado en esta tesis para evaluar los riesgos de privacidad. En cuanto a los mecanismos de privacidad explicables, se han diseñado utilizando técnicas de paternalismo blando y elementos de gamificación que hacen uso de
las métricas propuestas. Estos mecanismos se han integrado en la red social PESEDIA
y evaluado en experimentos con usuarios reales. PESEDIA es una red social desarrollada en el marco de la tesina de Master del doctorando [15], esta tesis y los proyectos
nacionales “Privacidad en Entornos Sociales Educativos durante la Infancia y la Adolescencia” (TIN2014-55206-R) y “Agentes inteligentes para asesorar en privacidad en
redes sociales” (TIN2017-89156-R).
Los resultados confirman la validez de las métricas propuestas para calcular el alcance
de los usuarios y la sensibilidad de las publicaciones de las redes sociales. En cuanto
a la métrica del alcance, los resultados también mostraron la posibilidad de estimarla
mediante métricas de centralidad local y social para escenarios con acceso limitado a
la información. En cuanto a la métrica de sensibilidad, los resultados también pusieron
de manifiesto la falta de concordancia de los usuarios en el caso de algunos tipos de informaciĂłn y el consenso en el caso de la mayorĂa de ellos. El uso de estas mĂ©tricas como
parte de los mensajes sobre las posibles consecuencias de las opciones de polĂtica de
privacidad y las acciones de intercambio de informaciĂłn a los usuarios mostrĂł efectos
positivos en el comportamiento de los usuarios con respecto a la privacidad. Además,
los resultados de la exploraciĂłn de la compensaciĂłn de los usuarios entre los costos y
los beneficios durante las acciones de divulgaciĂłn de informaciĂłn personal mostraron
relaciones significativas con los cĂrculos sociales habituales (familiares, amigos, compañeros de trabajo y usuarios desconocidos) y sus propiedades. Esto permitiĂł diseñar
mejores mecanismos de privacidad que restringen adecuadamente el acceso a la informaciĂłn y reducen los arrepentimientos. Por Ăşltimo, los elementos de gamificaciĂłn
aplicados a las redes sociales y a la privacidad de los usuarios mostraron un efecto positivo en el comportamiento de los usuarios hacia la privacidad y las prácticas seguras
en las redes sociales.[CA] En l’actualitat, les xarxes socials s’han convertit en un fenomen cultural dominant per
a milions d’usuaris d’Internet. Les xarxes socials són un entorn ideal per a la generació
de tota mena de beneficis socials per als usuaris. Els usuaris comparteixen experiències, mantenen el contacte amb els seus familiars, amics i coneguts, i obtenen beneficis
econòmics grà cies al poder de la seva influència (el que es tradueix en noves oportunitats de treball). No obstant això, l’ús de les xarxes socials i l’acció de compartir
informaciĂł implica la perduda de la privacitat dels usuaris.
Recentment ha emergit un gran interès per protegir la privacitat dels usuaris. Aquesta
situació s’ha degut als casos de penediments documentats en les accions dels usuaris,
escĂ ndols empresarials produĂŻts per usos indeguts de la informaciĂł personal, i als caires
que introdueixen els mecanismes de privacitat. Els proveĂŻdors de xarxes socials han inclòs millores en els seus sistemes per a reduir els riscos en privacitat dels usuaris; per exemple, restringint les polĂtiques de privacitat per defecte, afegint nous elements de configuraciĂł de la privacitat, i dissenyant accessos fĂ cils i directes per a configurar la privacitat dels usuaris. En el camp de la recerca de la privacitat, nous avanços es proposen
per a millorar els mecanismes de privacitat la majoria centrats en l’automatització,
selecciĂł de gra fi, i Ăşs de caracterĂstiques extretes de la informaciĂł i les seues interaccions per a recomanar la millor polĂtica de privacitat per a l’usuari. Malgrat aquests
avanços, molts estudis han demostrat que la preocupació dels usuaris per la privacitat
no es correspon amb les decisions que finalment prenen en les xarxes socials. Aquesta
desalineació en el comportament dels usuaris podria deure’s a la complexitat del propi
concepte de privacitat. Aquest inconvenient fa que els usuaris ignorin els riscos de privacitat, o els percebin com temporalment distants. Una altra causa de la desalineaciĂł
en el comportament dels usuaris podria deure’s a la complexitat del procés de presa de
decisions sobre la privacitat. Això es deu al fet que els usuaris han de considerar tots
els escenaris possibles i els factors involucrats (per exemple, el nombre d’amics, el tipus
de relaciĂł, el context de la informaciĂł, etc.) per a prendre una decisiĂł apropiada sobre
la privacitat.
Les principals contribucions d’aquesta tesi són el desenvolupament de mètriques per a
avaluar els riscos de privacitat, i la proposta de mecanismes de privacitat explicables
(fent ús de les mètriques desenvolupades) per a assistir i conscienciar als usuaris durant
el procés de decisió sobre la privacitat. Atesa la definició del concepte de la privacitat,
les dimensions de l’abast de la informació i la sensibilitat de la informació s’han considerat en aquesta tesi per a avaluar els riscos de privacitat. Respecte als mecanismes
de privacitat explicables, aquests s’han dissenyat utilitzant tècniques de paternalisme bla i elements de gamificació que fan ús de les mètriques propostes. Aquests mecanismes s’han integrat en la xarxa social PESEDIA i avaluat en experiments amb usuaris
reals. PESEDIA Ă©s una xarxa social desenvolupada en el marc de la tesina de MĂ ster del
doctorant [15], aquesta tesi i els projectes nacionals “Privacitat en Entorns Socials Educatius durant la Infà ncia i l’Adolescència” (TIN2014-55206-R) i “Agents Intel·ligents
per a assessorar en Privacitat en xarxes socials” (TIN2017-89156-R).
Els resultats confirmen la validesa de les mètriques propostes per a calcular l’abast de
les accions dels usuaris i la sensibilitat de les publicacions de les xarxes socials. Respecte a la mètrica de l’abast, els resultats també van mostrar la possibilitat d’estimarla mitjançant mètriques de centralitat local i social per a escenaris amb accés limitat
a la informació. Respecte a la mètrica de sensibilitat, els resultats també van posar
de manifest la falta de concordança dels usuaris en el cas d’alguns tipus d’informació
i el consens en el cas de la majoria d’ells. L’ús d’aquestes mètriques com a part dels
missatges sobre les possibles conseqüències de les opcions de polĂtica de privacitat i les
accions d’intercanvi d’informació als usuaris va mostrar efectes positius en el comportament dels usuaris respecte a la privacitat. A més, els resultats de l’exploració de la
compensaciĂł dels usuaris entre els costos i els beneficis durant les accions de divulgaciĂł
d’informació personal van mostrar relacions significatives amb els cercles socials habituals (familiars, amics, companys de treball i usuaris desconeguts) i les seves propietats. Això ha permés dissenyar millors mecanismes de privacitat que restringeixen
adequadament l’accés a la informació i redueixen els penediments. Finalment, els elements de gamificació aplicats a les xarxes socials i a la privacitat dels usuaris van
mostrar un efecte positiu en el comportament dels usuaris cap a la privacitat i les prà ctiques segures en les xarxes socials.Alemany Bordera, J. (2020). Measures of Privacy Protection on Social Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/151456TESISCompendi
Big Data Management Towards Impact Assessment of Level 3 Automated Driving Functions
As industrial research in automated driving is rapidly advancing, it is of paramount importance to
analyze field data from extensive road tests. This thesis presents a research work done in L3Pilot,
the first comprehensive test of automated driving functions (ADFs) on public roads in Europe.
L3Pilot is now completing the test of ADFs in vehicles by 13 companies. The tested functions are
mainly of Society of Automotive Engineers (SAE) automation level 3, some of level 4. The overall
collaboration among several organizations led to the design and development of a toolchain aimed
at processing and managing experimental data sharable among all the vehicle manufacturers to
answer a set of 100+ research questions (RQs) about the evaluation of ADFs at various levels,
from technical system functioning to overall impact assessment. The toolchain was designed to
support a coherent, robust workflow based on Field opErational teSt supporT Action (FESTA), a
well-established reference methodology for automotive piloting. Key challenges included ensuring
methodological soundness and data validity while protecting the vehicle manufacturers\u2019
intellectual property. Through this toolchain, the project set up what could become a reference
architecture for managing research data in automated vehicle tests. In the first step of the workflow,
the methodology partners captured the quantitative requirements of each RQ in terms of the
relevant data needed from the tests. L3Pilot did not intend to share the original vehicular signal
timeseries, both for confidentiality reasons and for the enormous amount of data that would have
been shared. As the factual basis for quantitatively answering the RQs, a set of performance
indicators (PIs) was defined. The source vehicular signals were translated from their proprietary
format into the common data format (CDF), which was defined by L3Pilot to support efficient
processing through multiple partners\u2019 tools, and data quality checking. The subsequent vi
performance indicator (PI) computation step consists in synthesizing the vehicular time series into
statistical syntheses to be stored in the project-shared database, namely the Consolidated Database
(CDB). Computation of the PIs is segmented based on experimental condition, road type and
driving scenarios, as required to answer the RQs. The supported analysis concerns both objective
data, from vehicular sensors, and subjective data from user (test drivers and passengers)
questionnaires. The overall L3Pilot toolchain allowed setting up a data management process
involving several partners (vehicle manufacturers, research institutions, suppliers, and developers),
with different perspectives and requirements. The system was deployed and used by all the relevant
partners in the pilot sites. The experience highlights the importance of the reference methodology
to theoretically inform and coherently manage all the steps of the project and the need for effective
and efficient tools, to support the everyday work of all the involved research teams, from vehicle
manufacturers to data analysts
Enhancing data privacy and security in Internet of Things through decentralized models and services
exploits a Byzantine Fault Tolerant (BFT) blockchain, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices\u2019 network traffic flows as blockchain transactions. Secondly, we take the challenge to decentralize IoT, and design a hybrid blockchain architecture for IoT, by proposing Hybrid-IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Connection among the PoW sub-blockchains employs a BFT inter-connector framework. We focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds
Towards sustainable e-learning platforms in the context of cybersecurity: A TAM-driven approach
The rapid growth of electronic learning (e-learning) platforms has raised concerns about cybersecurity risks. The vulnerability of university students to cyberattacks and privacy concerns within e-learning platforms presents a pressing issue. Students’ frequent and intense internet presence, coupled with their extensive computer usage, puts them at higher risk of being a potential victim of cyberattacks. This problem necessitates a deeper understanding in order to enhance cybersecurity measures and safeguard students’ privacy and intellectual property in educational environments. This dissertation work addresses the following research questions: (a) To what extent do cybersecurity perspectives affect student’s intention to use e-learning platforms? (b) To what extent do students’ privacy concerns affect their intention to use e-learning platforms? (c) To what extent does students’ cybersecurity awareness affect their intention to use e-learning platforms? (d) To what extent do academic integrity concerns affect their intention to use e-learning platforms? and (e) To what extent does students’ computer self-efficacy affect their intention to use e-learning platforms? This study was conducted using an enhanced version of the technology acceptance model (TAM3) to examine the factors influencing students’ intention to use e-learning platforms. The study involved undergraduate and graduate students at Eastern Michigan University, and data were collected through a web-based questionnaire. The questionnaire was developed using the Qualtrics tool and included validated measures and scales with close-ended questions. The collected data were analyzed using SPSS 28, and the significance level for hypothesis testing was set at 0.05. Out of 6,800 distributed surveys, 590 responses were received, and after data cleaning, 582 responses were included in the final sample. The findings revealed that cybersecurity perspectives, cybersecurity awareness, academic integrity concerns, and computer self-efficacy significantly influenced students’ intention to use e-learning platforms. The study has implications for practitioners, educators, and researchers involved in designing secure e-learning platforms, emphasizing the importance of cybersecurity and recommending effective cybersecurity training programs to enhance user engagement. Overall, the study highlights the role of cybersecurity in promoting the adoption and usage of e-learning platforms, providing valuable insights for developers and educators to create secure e-learning environments and benefiting stakeholders in the e-learning industry
A Psychometric Scale to Measure Individuals’ Value of Other People’s Privacy (VOPP)
Researchers invested enormous eforts to understand and mitigate the concerns of users as technologies collect their private data. However, users often undermine other people’s privacy when, e.g., posting other people’s photos online, granting mobile applications to access contacts, or using technologies that continuously sense the surrounding. Research to understand technology adoption and behaviors related to collecting and sharing data about non-users has been severely lacking. An essential step to progress in this direction is to identify and quantify factors that afect technology’s use. Toward this goal, we propose and validate a psychometric scale to measure how much an individual values other people’s privacy. We theoretically grounded the appropriateness and relevance of the construct and empirically demonstrated the scale’s internal consistency and validity. This scale will advance the feld by enabling researchers to predict behaviors, design adaptive privacy-enhancing technologies, and develop interventions to raise awareness and mitigate privacy risks
Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization
Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies with wide ranging applications, including surveillance, mapping, remote sensing, search and rescue, and disaster management. As sophisticated Unmanned Aerial Vehicle (UAV) increasingly operate in collaborative swarms, joint optimization challenges arise, such as flight trajectories, scheduling, altitude, Aerial Base Stations (ABS), energy harvesting, power transfer, resource allocation, and power consumption. However, the widespread adoption of UAV networks has been hindered by challenges related to optimal Three-Dimensional (3D) deployment, trajectory optimization, wireless and computational resource allocation, and limited flight durations when operating as ABSs. Crucially, the broadcast nature of UAV-assisted wireless networks renders them susceptible to privacy and security threats such as Distributed Denial-of-Service (DDoS) replay, impersonation, message injection, spoofing, malware infection, eavesdropping, and line of-interference attacks.
This study aims to address these privacy and security challenges by leveraging blockchain technology’s potential to secure data and delivery in UAV communication networks. With amalgamation of blockchain, this study seeks to harness its inherent immutability and cryptographic properties to ensure secure and tamper-proof data transmission, promote trust and transparency among stakeholders, enable automated Smart Contract (SC) for secure delivery, and facilitate standardization and interoperability across platforms. Specifically, blockchain can secure UAV network privacy and security through data privacy and integrity, secure delivery and tracking, access control, identity management, and resilience against cyber-attacks.
Furthermore, this study explores the synergies among blockchain, UAV networks, and Federated Learning (FL) for privacy-preserving intelligent applications in healthcare and wireless networks. FL enables collaborative training of Machine Learning (ML) models without sharing raw data, ensuring data privacy. By integrating FL with blockchain-assisted UAV networks, this study aims to revolutionize future intelligent applications, particularly in time-sensitive and privacy-critical domains. Overall, this thesis contributes to the field by providing a comprehensive analysis of integrating blockchain, FL, and UAV networks, beyond Fifth-Generation (5G) communication networks. It addresses privacy and security concerns related to data and delivery, thereby enabling secure, reliable, and intelligent applications in various sectors
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