Wireless networks collect vast amounts of log data concerning usage of the network. This data aids in informing operational needs related to performance, maintenance, etc., but it is also useful for outside researchers in analyzing network operation and user trends. Releasing such information to these outside researchers poses a threat to privacy of users. The dueling need for utility and privacy must be addressed. This thesis studies the concept of differential privacy for fulfillment of these goals of releasing high utility data to researchers while maintaining user privacy. The focus is specifically on physical user trajectories in authentication manager log data since this is a rich type of data that is useful for trend analysis. Authentication manager log data is produced when devices connect to physical access points (APs) and trajectories are sequences of these spatiotemporal connections from one AP to another for the same device. The fulfillment of this goal is pursued with a variable length n-gram model that creates a synthetic database which can be easily ingested by researchers. We found that there are shortcomings to the algorithm chosen in specific application to the data chosen, but differential privacy itself can still be used to release sanitized datasets while maintaining utility if the data has a low sparsity. / Master of Science / Wireless internet networks store historical logs of user device interaction with it. For example, when a phone or other wireless device connects, data is stored by the Internet Service Provider (ISP) about the device, username, time, and location of connection. A database of this type of data can help researchers analyze user trends in the network, but the data contains personally identifiable information for the users. We propose and analyze an algorithm which can release this data in a high utility manner for the researchers, yet maintain user privacy. This is based on a verifiable approach to privacy called differential privacy. This algorithm is found to provide utility and privacy protection for datasets with many users compared to the size of the network.

Recently, community initiatives have emerged for the establishment of large-scale environmental observatories. Cyberinfrastructure is the backbone upon which these observatories will be built, and scientists' ability to access and use the data collected within observatories to address research questions will depend on the successful implementation of cyberinfrastructure. The research described in this dissertation advances the cyberinfrastructure available for supporting environmental observatories. This has been accomplished through both development of new cyberinfrastructure components as well as through the demonstration and application of existing tools, with a specific focus on point observations data. The cyberinfrastructure that was developed and deployed to support collection, management, analysis, and publication of data generated by an environmental sensor network in the Little Bear River environmental observatory test bed is described, as is the sensor network design and deployment. Results of several analyses that demonstrate how high-frequency data enable identification of trends and analysis of physical, chemical, and biological behavior that would be impossible using traditional, low-frequency monitoring data are presented. This dissertation also illustrates how the cyberinfrastructure components demonstrated in the Little Bear River test bed have been integrated into a data publication system that is now supporting a nationwide network of 11 environmental observatory test bed sites, as well as other research sites within and outside of the United States. Enhancements to the infrastructure for research and education that are enabled by this research are impacting a diverse community, including the national community of researchers involved with prospective Water and Environmental Research Systems (WATERS) Network environmental observatories as well as other observatory efforts, research watersheds, and test beds. The results of this research provide insight into and potential solutions for some of the bottlenecks associated with design and implementation of cyberinfrastructure for observatory support.

Conferencia realizada en el marco de la "III Conferencia Latinoamericana de Analíticas de Aprendizaje LALA2020 Project", del 1 al 2 de Octubre de 2020 en Cuenca, Ecuador. / The aim of this paper is to investigate the perceptions of learning analytics re-searchers in Peru about the barriers to publication of their research data. A review of the relevant legislation was done. Semi-structured interviews were used as a research method, the focus being on the presumed conflict between the publica-tion of research data and the protection of personal data. The results show a range of individual factors that influence the behaviour of scientists in relation to the publication of research data, emphasizing the barriers related to data protection in different disciplines.

The aim of this master thesis is description of creating application designated to improve orienta-tion in radio spectrum usage in the Czech Republic and publication of data used by application as open data. First, the thesis describes open data, its importance and method of working with it. This part is followed by an analysis of issues, analysis of available data and selection of methodology for appli-cation development as well as for open data publication. Description of the process of application development and its launch follows. Whole thesis concludes description of the process for open data publication.

This Ph.D. thesis deals with Open Government Data and the methodology for publication of this kind of data. Public sector bodies hold a significant amount of data that can be reused in innovative way leading to development of new products and services. According to the Open Knowledge Foundation "Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike." Publication and reuse of Open Government Data can lead to benefits such as increased economic growth. State, society as well as the public sector bodies themselves can benefit from Open Government Data. However the public sector bodies currently face a number of problems and issues when publishing Open Government Data, e.g. regular updates of the published datasets are not always ensured. Different public sector bodies apply different approaches to publication of Open Government Data. The main goal of this thesis is to design the Open Government Data Publication Methodology which should address current problems related to the publication of Open Government Data.

Diese Studie untersucht umfassend Barrieren bei der Veröffentlichung von Forschungsdaten im Bereich Learning Analytics (LA) mithilfe eines Mixed-Methods-Ansatzes. Methodologisch gegliedert in vier Phasen – Systematic Literature Review (SLR), Leitfrageninterviews, eine weltweite Online-Umfrage und adaptive Workshops – zeigt die Forschung eine Vielzahl interdisziplinärer und internationaler Perspektiven auf. Das SLR bildet die Grundlage, indem es rechtliche, ethische und ressourcenbezogene Hindernisse für die Datenveröffentlichung identifiziert. Durch die Integration dieser Erkenntnisse in Interviews zeigt sich ein vertieftes Verständnis kultureller und institutioneller Unterschiede, die die Datenpublikation beeinflussen. Eine globale Umfrage verdeutlicht zudem eine Diskrepanz zwischen der Bereitschaft von Forschenden, Daten zu teilen, und ihrer Bewertung der Vorteile geteilten Wissens. Dies weist auf Vertrauensthemen und den geringen wahrgenommenen Nutzen gemeinsamer Daten in der Forschung hin, trotz zunehmender Infrastrukturen und Förderungen für Open Data. Adaptive Workshops beleuchten die Lücke zwischen der Anerkennung der Bedeutung von Datenfreigabe und der Fähigkeit der Forschenden, diese effektiv umzusetzen. Insbesondere Datenschutzbedenken, etwa zur DSGVO, und der Verlust von Kontrolle über geteilte Daten erweisen sich als große Hürden. Die Ergebnisse dieser Studie verdeutlichen, wie Barrieren der Datenpublikation je nach Disziplin und Region variieren und tief in kulturellen und institutionellen Rahmen eingebettet sind. / This study investigates barriers to research data publication in Learning Analytics (LA) through a mixed-method approach encompassing a Systematic Literature Review (SLR), semi-structured interviews, a global survey, and adaptive workshops. The SLR establishes a foundation by identifying legal, ethical, and resource-related barriers to data publication across disciplines. Findings from the SLR integrate in the subsequent interviews, which reveal cultural and institutional nuances affecting researchers' motivations and capabilities for data sharing. A global survey uncovers a discrepancy between researchers' willingness to share data and their perceived benefits from accessing others' data, highlighting trust issues within the scientific community despite growing support for open data. Adaptive workshops underscore the gap between researchers' recognition of data sharing importance and their practical ability to implement it, with data protection concerns, particularly related to GDPR compliance, emerging as major barriers alongside fears of losing data control. The findings from this study illustrate how barriers to data publication vary by discipline and region, being deeply embedded within cultural and institutional frameworks.