Diese Dissertation besteht aus drei unabhängigen Kapiteln, die sich mit Wissen und Informationen in der mikroökonomischen Theorie beschäftigen. In Kapitel 1 untersuchen wir ein Duopolmodell mit Preisdiskriminierung, bei dem die Verbraucher über ihren Datenschutz entscheiden. Wir stellen zwei Datenumgebungen gegenüber und finden für jede ein Gleichgewicht. In einer offenen Datenumgebung geben alle Verbraucher ihre Daten preis. Unternehmen diskriminieren bei der Preisgestaltung, was zu Wohlfahrtsverlusten aufgrund von Abwerbung führt. In einer Umgebung mit exklusiven Daten anonymisieren sich die Verbraucher, die Preise sind einheitlich, und der Markt ist effizient. Wir testen die Gleichgewichte in einem Experiment. In Kapitel 2 untersuchen wir ein Modell einer Organisation, die wissensintensive Produktion betreibt. Der Organisationsdesigner stellt Arbeiter ein, die mit Wissen ausgestattet sind, um Probleme zu lösen, deren Art ex ante unbekannt ist. Der Designer bestimmt, ob die Arbeitnehmer einzeln oder im Team produzieren. Als Team können die Arbeitnehmer kommunizieren und ihr Wissen teilen, während sie bei Einzelarbeit nur ihr eigenes Wissen nutzen können. Wir stellen fest, dass Teamarbeit optimal ist, wenn Spillovers ausreichend hoch sind. Insbesondere dann, wenn Spillovers perfekt oder alle Problemtypen gleich wahrscheinlich sind, sind selbstverwaltete Teams optimal. In Kapitel 3 untersuche ich ein dynamisches Modell mit einem Moral-Hazard-Problem und einem kostspieligen Wissenstransfer. Ein Auftraggeber stellt zwei risikoneutrale, vermögensbeschränkte Agenten ein, die jeweils eine individuelle Aufgabe in einem Projekt übernehmen. Bevor sie sich ihren Aufgaben zuwenden, können die Agenten beschließen, Wissen zu transferieren, das die Produktivität des Empfängers erhöht. Der Auftraggeber kann durch ein gemeinsames Leistungssignal einen Transfer mit oder ohne Verpflichtungsmacht veranlassen. / This dissertation consists of three independent chapters that contribute to understanding how knowledge and information is used in microeconomic theory. In Chapter 1, we study a duopoly model of behavior-based pricing where consumers decide on their data privacy. Contrasting two data environments, we find unique equilibria for each. In an open data environment, all consumers reveal their data. Firms price discriminate causing welfare losses due to poaching. In an exclusive data environment, consumers anonymize, prices are uniform, and the market is efficient. We test the predictions in an experiment. In the open data treatment, subjects act as predicted. In the exclusive data treatment, buyers initially share data but anonymize when sellers poach. In Chapter 2, we study a model of an organization engaging in knowledge-intensive production. The organizational designer hires workers endowed with knowledge to solve problems whose types are ex ante unknown. The designer determines whether workers produce individually or as team. As team, workers can communicate and share their knowledge, while when working individually they can only use their own knowledge. We find that teamwork is optimal when spillovers are sufficiently high. Particularly, when spillovers are perfect, or all problem types are equally likely, self-managed teams arise as a special form of teamwork. In Chapter 3, I explore a dynamic model with a moral hazard problem and knowledge transfer. A principal hires two risk-neutral, wealth-constrained agents to each perform an individual task in a project. Before they address their tasks, the agents can decide to transfer knowledge that increases the task-related productivity of the receiver. The transfer is costly for both. I find that the principal can induce a transfer with or without commitment power through a joint performance signal. It is not clear that commitment is always better, even though with commitment the first-best allocation can be achieved.

This study focuses on online news coverage of TikTok's privacy policies to uncover accusations related to security threats between October 2020 and May 2023. Using critical discourse analysis, the research compares TikTok's discourse with other platforms like Facebook and YouTube, highlighting the role of governments, tech companies, and users in shaping this discourse. Furthermore, it demonstrates how cultural and political factors influence privacy discussions, particularly regarding the controversies, discussions, and accusations between the United States and China. AI tool ChatGPT analyzes the discourse by focusing on the news texts' most prominent topics and keywords. The goal is to identify key themes for each highlighted keyword to answer the following questions: (1) How does TikTok's privacy policy construct notions of privacy and security? (2) How do shareholders discuss potential risks in TikTok’s privacy policies? (3) What are the risks of privacy violations and TikTok's global implications? This research asserts that when analyzed alongside the privacy policy discourses of other major social media platforms, TikTok's privacy policies reveal significant implications for cybersecurity, particularly in the context of informed consent. The findings highlight the interplay of cultural, political, and economic factors, emphasizing the urgent need for continuous monitoring and accountability in digital privacy-seeking risk assessment and minimizing.

Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.

Data privacy has been growing in importance in recent years, especially with the continuous increase of online activity. Researchers study, design, and develop solutions aimed at enhancing users’ data privacy. The wicked problem of data privacy is a continuous challenge that defies straightforward solutions. Since there are many factors involved in data privacy, such as technological, legal, and human aspects, we can only aim at mitigating rather than solving this wicked problem. Our aim was to focus on human aspects for designing usable crypto-based privacy-enhancing solutions.  In this thesis, we followed a user centered design method by using empirical qualitative means for investigating user’s perceptions and opinions of our solutions. Most of our work has focused on redactable signatures in the cloud context within the eHealth use-case. Redactable signatures are  a privacy enhancing scheme allowing to remove parts of a signed document by a specified party for achieving data minimization without invalidating the respective signature. We mainly used semi-structures interviews and focus groups in our investigations. Our results yielded key HCI considerations as well as guidelines of different means for supporting the design of future solutions. / Data privacy has been growing in importance in recent years, especially with the continuous increase of online activity. Researchers continuously study, design, and develop solutions aimed at enhancing users’ data privacy. The wicked problem of data privacy is the continuous challenge that defies straightforward solutions. Since there are many factors involved in data privacy, such as technological, legal, and human aspects, we can only aim at mitigating rather than solving this wicked problem. Our aim was to focus on human aspects for designing usable crypto-based privacy-enhancing solutions.  In this thesis, we followed a user centered design method by using empirical qualitative means for investigating user’s perceptions and opinions of our solutions. Most of our work has focused on redactable signatures in the cloud context within an eHealth use-case. Redactable signatures are a privacy-enhancing scheme, which allow the removal of parts of a signed document by a specified party without invalidating the respective signature. Our results yielded key HCI considerations as well as guidelines of different means for supporting the design of future solutions. / <p>Paper 3 was included as manuscript in the thesis.</p>

Data anonymization is a relevant and important field within data privacy, which tries to find a good balance between utility and privacy in data. The field is especially relevant since the GDPR came into force, because the GDPR does not regulate anonymous data. This thesis focuses on anonymization of directory-structured data, which means data structured into a tree of directories. In the thesis, four of the most common models for anonymization of tabular data, k-anonymity, ℓ-diversity, t-closeness and differential privacy, are adapted for anonymization of directory-structured data. This adaptation is done by creating three different approaches for anonymizing directory-structured data: SingleTable, DirectoryWise and RecursiveDirectoryWise. These models and approaches are compared and evaluated using five metrics and three attack scenarios. The results show that there is always a trade-off between utility and privacy when anonymizing data. Especially it was concluded that the differential privacy model when using the RecursiveDirectoryWise approach gives the highest privacy, but also the highest information loss. On the contrary, the k-anonymity model when using the SingleTable approach or the t-closeness model when using the DirectoryWise approach gives the lowest information loss, but also the lowest privacy. The differential privacy model and the RecursiveDirectoryWise approach were also shown to give best protection against the chosen attacks. Finally, it was concluded that the differential privacy model when using the RecursiveDirectoryWise approach, was the most suitable combination to use when trying to follow the GDPR when anonymizing directory-structured data.

Data surveillance is the systematic use of personal data systems in the investigation or monitoring of the actions or communications of one or more persons. This collection of papers was the basis for a supplication under Rule 28 of the ANU's Degree of Doctor of Philosophy Rules. The papers develop a body of theory that explains the nature, applications and impacts of the data processing technologies that support the investigation or monitoring of individuals and populations. Literature review and analysis is supplemented by reports of field work undertaken in both the United States and Australia, which tested the body of theory, and enabled it to be articulated. The research programme established a firm theoretical foundation for further work. It provided insights into appropriate research methods, and delivered not only empirically-based descriptive and explanatory data, but also evaluative information relevant to policy-decisions. The body of work as a whole provides a basis on which more mature research work is able to build.

Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The term {em data obfuscation} is used to refer to the class of algorithms that modify the values of the data items without distorting the usefulness of the data. The main goal of this thesis is the development of a data obfuscation technique that provides robust privacy protection with minimal loss in usability of the data. Although medical and financial services are two of the major areas where information privacy is a concern, privacy breaches are not restricted to these domains. One of the areas where the concern over data privacy is of growing interest is collaborative filtering. Collaborative filtering systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. The prediction accuracy of these systems is dependent on the size and accuracy of the data provided by users. However, the lack of sufficient guidelines governing the use and distribution of user data raises concerns over individual privacy. Users often provide the minimal information that is required for accessing these E-commerce services. The lack of rules governing the use and distribution of data disallows sharing of data among different communities for collaborative filtering. The goals of this thesis are (a) the definition of a standard for classifying DO techniques, (b) the development of a robust cluster preserving data obfuscation algorithm, and (c) the design and implementation of a privacy-preserving shared collaborative filtering framework using the data obfuscation algorithm.

The ability for domestic consumers to provide demand response to dynamic electricity pricing will become increasingly valuable for integrating the high penetrations of renewables that are expected to be connected to electricity networks in the future. The aim of this thesis is to investigate whether domestic consumers will be willing and able to provide demand response in such low-carbon futures. A broad approach is presented in this thesis, with research contributions on subjects including data privacy, behavioural economics, and battery modelling. The principle argument of the thesis is that studying the behaviour of consumers with grid-connected photovoltaic ('PV') systems can provide insight into how consumers might respond to dynamic pricing in future low-carbon power systems, as both experience irregular electricity prices that are correlated with intermittent renewable generation. Through a combination of statistical and qualitative methods, this thesis investigates the demand response behaviour of consumers with PV systems in the UK. The results demonstrate that these consumers exhibit demand response behaviour by increasing demand during the day and decreasing demand during the evening. Furthermore, this effect is more pronounced on days with higher irradiance. The results are novel in three ways. First, they provide quantified evidence that suggests that domestic consumers with PV systems engage in demand response behaviour. Second, they provide evidence of domestic consumers responding to irregular electricity prices that are correlated with intermittent renewable generation, thereby addressing the aim of this thesis, and supporting the assumption that consumers can be expected to respond to dynamic pricing in future markets with high penetrations of renewables. Third, they provide evidence of domestic consumers responding to dynamic pricing that is similar to real-time pricing, while prior evidence of this is rare and confined to the USA.

Dienstleister in der Telekommunikationsbranche haben nicht zuletzt aus rechtlicher Sicht die Pflicht zu einem sensiblen Umgang mit personenbezogenen Daten. Dies bezieht sich nicht nur auf Kundendaten, sondern ebenso auf mitarbeiterbezogene Daten zur Führung eines Call Centers. Je nach Situation und Anwendungsfall regeln die Verwendungsmöglichkeiten dieser Daten in Call Centern das allgemeine Persönlichkeitsrecht und das Bundesdatenschutzgesetz (BDSG). Daraus ergibt sich für die Entwicklung und den Einsatz von Call Center-spezifischen Anwendungssystemen (z.B. Kampagnenmanagement-Systeme, Dialer) die Herausforderung, zum einen die Einhaltung rechtlicher Bestimmungen sicherzustellen, aber zum anderen den häufig detailreichen Informationsbedarfen der Call Center-Leitungsebenen zu entsprechen. Neben rechtlichen Beschränkungen bei der Handhabung von Kundendaten sind hier die Grenzen und Grauzonen bezüglich der Verwendungsmöglichkeiten von Leistungsdaten zur Mitarbeiterüberwachung und -beurteilung (z.B. verdecktes Mithören oder Gesprächsaufzeichnung) zu berücksichtigen.

Increasingly, online social media networks allow users to use geotagging. This method of adding location data to various content shared in real time has introduced privacy related issues and threats to the users of such networks. Previous research present opposing findings on whether users actually care about their location privacy or not, and it has also been shown that users often display a behaviour inconsistent with their concerns. When asked, users tend to report high privacy concerns, but in contrast, they will then not let their privacy concerns affect or limit their behaviour online; the privacy paradox is a description of this dichotomy. The problem, however, is not only that location privacy seems to be a paradoxical issue; the sharing of location data provides users with new possibilities that can potentially have negative consequences for them, such as someone else being able to identify one’s identity, home location, habits or other sensitive information. Social media network users communicate that a part of this is due to the lack of control over which information they share, with whom and where.This study employs a qualitative method, using unstructured interviews in a pre-study and a self-completion questionnaire. The purpose of the study is to examine and gain a better understanding of how the privacy paradox can help to better explain users’ location data disclosure preferences in the context of social media networking, and to help social media network developers in order to reduce privacy-related issues in social media networking applications with geotagging capabilities. The findings indicate that the paradox indeed is evident in user’s stated geotagging behaviour, and that users are slightly more worried about their location privacy than their overall online privacy. The conclusions offer a couple of different explanations for the paradox, and we argue that the contradiction of the paradox can be seen as a constant trade-off between benefits and risks of geotagging. We also give some examples of such advantages and disadvantages.