Governments across the world looking to implement Open Government Data (OGD) initiatives undergo many problems. One such problem is the risk to privacy from opening data sets as most of the data is at a microdata level which corresponds to specific individuals. A solution to such a predicament is the application of Statistical Disclosure Control (SDC) techniques on microdata. SDC methods anonymize microdata that reduces the risk of disclosure while also maintaining the value of the data. SDC methods can be applied by using software tools, however, these tools are designed from the perspective of experts or for the purpose of demonstration. Moreover, ongoing research has led to the slow progress in not only the development of these tools, but also their adoption. Resulting in limited support material and even smaller user base. As a consequence, individuals or organizations looking to adopt these tools to satisfy their data privacy objectives cannot use them. Out of many SDC tools, ARX is a stable application that equips its users with an arsenal of techniques to anonymize microdata sets. It also undergoes regular updates, thus keeping pace with the current developments in the field of SDC techniques. Despite this, ARX is not widely used due to its perceived complexity. This thesis addresses the problem of the complexity that is associated with ARX which makes it difficult to adopt them to anonymize their data sets. The thesis provides a solution to this problem by developing a prototype tool which reduces the complexity of SDC techniques through a simplified, user-friendly approach to data anonymization. The thesis does not aim to enhance privacy methods or improve the functionalities of already existing tools by proposing a replacement. The thesis tries to bridge the gap which implicitly occurs when privacy tools are designed from the perspective of experts. It is understood that the protection of private data should only be handled by experts. However, to build that expertise, people have to be introduced to simpler tools without being overwhelmed by the complexity that is immanent with concepts of SDC.

There is a need for a more comprehensive sociotechnical systems view on ML. Such a view looks at the development and use of an ML system in practice as being a sociotechnical ML system: "a system consisting of technical artefacts, human agents and institutions, in which a machine-based subsystem influences its real or virtual environment by automating, supporting or augmenting decision-making". This research takes on this view to design a sociotechnical guide for ML practice, centring the specication of sociotechnical ML systems. Taking on the guidelines contributes to a safe and effective development and use of ML systems.

Cloud computing is one of the state-of-the-art technologies that has led to considerable transformations in an organization. Because of its numerous benefits like scalability, cost-efficiency, and security many organizations are migrating to the cloud. However, the process of migration is complex and faces numerous challenges. This includes numerous technical and non-technical challenges that impact the security of the organization. This study revealed a considerable research gap in the literature using the people, process and technology framework. The gap revealed that researchers and organizations focus mainly on technical challenges and weigh in the security risks triggered by them. The non-technical challenges like lack of resources, knowledge sharing, limited stakeholder management are overlooked. This study showed that these issues could also cause security risks like data loss, vulnerable systems, and unauthorized access to systems and data in the migration process. Therefore, the concept was designed by prioritizing the requirements from these non-technical challenges. Four essential features were discussed as a part of the solution: knowledge base, information centre, planning & stakeholder management, and learning. The suggested concept was evaluated and showed that organizations need to enable knowledge sharing, open feedback and stakeholder management in the initial phase of migration to mitigate security risks from the beginning.

Ultra-Wideband (UWB) technology became unregulated within the EU in 2007. Most recently, it was integrated into mobile phones in 2019, notably Apply and Samsung adding it to all their newer models. While UWB is characterised as a radio technology with any signal above 500 MHz, it operates within the 6-9 GHz range in mobile phones. This allows for fast data rate, low power secure communication, multipath facilities and accurate localization. While the integration of UWB is mostly advantageous to users and innovators, its ability of accurate localisation may lead to severe privacy concerns The aim of the thesis is to understand the privacy concerns of UWB’s integration into mobile phones by answering the main research question: how do experts and users perceive privacy concerns of UWB usage in mobile phones; and how can they be mitigated? It was subsequently broken down into three sub-research questions: 1. What are the possible applications of UWB in mobile phones? Phones have other incumbent radio technology embedded such as Bluetooth (BLE) and Wi-Fi, however it seems like UWB is being integrated to serve additional purposes. The answer to this question seeks to understand from gray and research literature how UWB can be used in mobile phones and what advantage it gives over incumbent technology. Research shows UWB gives phones the ability for indoor navigation, gesture-based control, foot traffic analysis for smart retail, teleconference systems, proximity-based localization, key-less entry among others. This leads to research question 2. What are the potential privacy concerns associated with UWB? The incorporation of new technology capable of accurate localization leads to privacy concerns. All privacy issues were categorised on the basis of three paradigms: social, surveillance and institutional mentioned in Gurses and Diaz, 2013. This was initially done by interviewing experts from the three groups of privacy experts, policy regulators and technology experts. Analysis of their answers showed that UWB privacy concerns seem relatively similar to BLE and Wi-Fi localization, albeit with higher granularity. UWB allows mobile phones companies, third parties and governments track people accurately indoors, push advertisements depending on location, obtain relative relationships between people based on distance leaving people with no place to hide. Subsequently, user interviews were carried out to see if they could identify the same concerns of UWB. Results showed that that from the data of users interviewed, all of them believed that accurate data localization of people is crossing a line that users cannot push back on. A majority of them saw most of the same privacy issues as the experts showing that, as people get more adept with technology they understand how it can affect their privacy. A common question that was asked across all the interviews was how can we protect our privacy in the face of such penetrating innovation as time lapses. Which is the final sub-research question: 3. What are technical and societal approaches to address privacy concerns? Experts provided solutions that were more industry oriented which included decoupling UWBfrom other location-based services, provision of opt-out settings on a more prominent basis, reworking license agreements, industry wide discussion and self-regulation in terms of privacy. However, users gave answers that were more user-centric and gave more control to the common public. This included users neggotiating their own privacy agreements, compensation models for loss of privacy, a more holistic regulation process and finally, trying to break the control of big tech companies. This shows that users and experts have very similar understanding of privacy issues but very different views on how privacy should be protected. Perhaps, it may be time for regulators to pay heed to user suggestions. These suggestions were then compared with privacy mitigation strategies mentioned in literature. Notably, the most overarching concept that needs to be incorporated is the concept of Privacy-by-design which can then be broken down into technical and societal strategies. Technical approaches included concepts such as obfuscation, k-anonymiser, differential privacy, dummy localization and access control mechanisms. All the technical strategies seemingly had the same issue of requiring third-party applications to function. Sophisticated security measures and privacy statements would then be needed to ensure these companies do not choose monetary gain over user privacy. Societal approaches included concepts of data-for-all, technical regulatory bodies and finally, breaking up of big tech companies. As time passes and innovations become more pervasive, it may be too late to incorporate privacy protection actively. The time to protect privacy is now.

Privacy-enhancing technologies (PETs) have historically been used for safeguarding individual privacy from both public and private interference. But lately, tech companies have started using PETs as one instrument for the expansion of their power over different actors, as appears to be unfolding in the case of Amazon’s Sidewalk service: a United States-only privacy-preserving crowdsourced service that promises connectivity to Internet of Things (IoT) devices manufactured by third parties in smart-home, logistics, and utilities use-cases. Compatible IoT devices (‘endpoints’) are granted connectivity by ‘gateways’, namely smart-home devices from Amazon’s Echo (smart speakers) and Ring (smart cameras and doorbells) series that donate a portion of their bandwidth to endpoints that might be owned by others. Amazon pushed a software update to these Echo and Ring devices, that turned them from smart-home devices to contributors to the Sidewalk network, unless users actively opted out, yielding a coverage of at least 90% of the US population. With Sidewalk, Amazon leverages PETs (namely end-to-end encryption and device identifier obfuscation) to mitigate privacy concerns that the crowdsourced architecture yields. However, this necessitates significant investments from third-party manufacturers to make their devices Sidewalk-compatible, suggesting a power emergence shaped by PETs. I answered the research question “How does Amazon’s use of privacy-enhancing technologies in Sidewalk affect its power over IoT manufacturers?” by reviewing grey literature, analysing the Sidewalk technology, and elite interviewing with high-ranking employees of Sidewalk-adopting manufacturers. I have shown that Amazon leveraged PETs to mitigate public security concerns, but in the meantime reshapes how manufacturers produce their devices. Part of this ploy is cementing AWS in their production processes. Amazon also uses this leverage to mobilise manufacturers’ and silicon providers’ resources to improve Sidewalk’s public reception, technology, and governance. These reconfigurations are expensive and complicated to realise, but manufacturers stressed the importance of Sidewalk adoption to leverage Amazon’s reputation vis-à-vis suppliers and customers, and “befriend the giant” for they rely on Amazon’s Marketplace, cloud, and logistics. Meanwhile, Amazon’s reductionist framing of privacy and security as protecting user identity and data confidentiality, means that confidentiality of manufacturers’ business-sensitive information is not discussed. With this vantage point, Amazon can learn which endpoint types are popular and how they work; but Sidewalk might also be a vehicle for Amazon to attract more IoT developers to AWS. In sum, I have demonstrated that strictly pursuing user privacy (or confidentiality) in digital services may have unforeseen effects on production. Therefore, I call upon privacy and competition scholars, advocates, and regulators to question how privacy protection actually augments companies’ power, and stepping away from their narrow “consumer harm” lenses. These actors should debate a right to personal control over devices. A mere consumer focus in studying these developments is insufficient: I established that business-to-business relations and businesses’ production processes are more significantly affected than consumers. The production focus of this work lays bare the novel power dynamics between Amazon and manufacturers, shaped by PETs.

In a globalized world, the rapid development in digital technologies and finance has enabled the successful growth of Airbnb around the world. The basic idea behind this growth is to take advantage of the underused assets —houses, apartments, or rooms and to profit from them. The spectacular growth in main cities has caused the social fabric of the city was negatively impacted. The diminishing local welfare, shortage in the housing market, and to the need for identifying ways to regulate the operations are the major concerns of local government who perceive Airbnb as a disruptor of the main city sub-systems —social, urban infrastructure, and governance. In this regard, it is important to understand the evolution in the adoption of Airbnb to explain the conditions that enabled the rapid absorption. Moreover, how people perceive houses as a profitable source of additional income becomes relevant in the discussion of commodification of housing. As a result, the connection with gentrification as an urban process that relates socio-economic conditions and the housing system emerges as a possible connection with Airbnb. The available data on Airbnb, households, and houses allows analyzing the potential correlations between Airbnb and the socio-economic conditions. This can help the decision-makers to understand the relationship between the role of Airbnb and the socio-economic conditions of a city. From a theoretical idea about the causes, effects, and facets of gentrification, this dissertation aims to bridge the gap between Airbnb as a socio-technical platform and the commodification of housing. This study focuses on three main aspects. Firstly, the gentrification theory is revisited in order to determine the dimensions of gentrification that can be measured. The proposal is based on the measurable characteristics of households and housing. Consequently, income as a proxy of socio-economic conditions is used to identify gentrified neighborhoods. Moreover, a set of novel indicators are derived to quantify the main changes in household socio-economic characteristics and housing dynamics. The framework developed can be adapted to different city cases around the world. To study the relationships this dissertation applies the framework to the case study of Amsterdam by using data from 2007 to 2018. Second, Airbnb has not evenly spread in the city. The concentration in some areas leads to think that there are neighborhoods more impacted than others. Specifically, the convergence of Airbnb operations and the gentrification process is important for finding potential relationships. The web-scrapped data from Airbnb for Amsterdam is used to make cross-comparisons with the indicators in gentrified neighborhoods from 2015 to 2018. The analysis focused on the total listings, prices, and expected revenue aggregated at the neighborhood level. Additionally, an analysis of the propensity of short-term rentals given the long term rentals is carried out to identify how Airbnb exert pressures in the existing housing system. The geographical visualization of results helps to identify the main relationships. Thirdly, To shift from the idea that houses are part of common needs to be a profitable income source represents a challenge for urban planners and governments. Therefore, since gentrification and Airbnb are urban phenomenons with potential impacts in the housing system, the research intends to find insights about the link with the commodification of housing. The analysis of relationships between the household characteristics and housing dynamics with Airbnb revenue aims to identify how some segments of the population benefit more than others. In this regard, three log-linear regression models explain the behavior in gentrified neighborhoods, other neighborhoods, and Amsterdam's city. The comparison is carried out using the growth percentage derived from the regressions to make the effect comparable. In total 30 neighborhoods out of 98 were identified as gentrified by income growth by applying the methodology proposed. The first comparisons showed that gentrified neighborhoods have more Airbnb listings, higher prices per night, and revenue per year. However, gentrified neighborhoods with low and average income show higher revenues than high income. Moreover, the analysis of the price growth reveals that 11 neighborhoods coincide with neighborhoods gentrified by income. Besides, districts of Oost, Zuid, West, and Noord contains neighborhoods with this condition. The detailed analysis using the indicators show that short-term rentals are unevenly distributed in the city. Further, this distribution is related to household characteristics and housing dynamics. In particular, the short-rentals growth per year is related to the characteristics of household compositions, age, ethnicity, migration, education level, housing living characteristics, property valuation, and property age. Further, the expected revenue per year shows relationships with these characteristics. However, aspects such as the neighborhood level of privatization are inversely correlated with the revenue in gentrified neighborhoods. Moreover, the growth in Airbnb revenue in gentrified neighborhoods helps identify potential rent gaps and opportunities to exacerbate gentrification patterns. There are relationships between Airbnb and gentrification, which goes beyond the increment in rent prices. The quantitative analysis showed that some populations are profiting more than others because Airbnb has spread in neighborhoods with specific characteristics. For instance, neighborhoods with higher percentages of young-adults of western origin and highly educated are receiving more benefits from Airbnb. Moreover, Airbnb's concentration is characterized by neighborhoods with relatively small living spaces with medium property values. These findings help understand that people in neighborhoods gentrified are getting more benefits for Airbnb. Besides, these neighborhoods are also characterized by high social mobility and small-medium-sized living spaces with fewer private owners. Consequently, rental prices can increase because the housing market is pressured in two ways; by the ongoing gentrification and the exacerbation of short-term rentals. In this regard, people in these areas can perceive Airbnb as an incentive to shift the living house condition to an economic one. Based on these findings, the municipality needs to revisit the regulation imposed on the whole short-term rentals. The necessity to evaluate the differential impact per neighborhood matters because some specific populations and houses have more propensity for Airbnb.

Email communication is a crucial part of the daily processes of enterprises. Organizations can opt for traditional infrastructure on-premise or use cloud-based email services provided by (foreign) cloud service providers. In Europe in particular, organizations from crucial sectors have been adopting cloudbasedemail services. The level of cloud adoption can vary strongly within these sectors. Nevertheless, this trend towards the use of cloud-based email services brings societal implications for the sovereignty of European data. Email services hosted with foreign cloud service providers can be susceptible to surveillance by foreign governments and intelligence agencies, which violates privacy of European individuals. The attack space further includes invasion with political and monetary incentives that may also impact security, as data is hosted with cloud service providers who might have weak security protocols. We measured the level of cloud adoption for seven crucial sectors in Europe: executive governments, healthcare, SME’s, higher educational institutes, NGO’s and financial services. We have conducted a DNS analysis on MX records from a Farsight (SIE) dataset to measure the prevalence of cloud service providers. The results revealed the prevalence of extremely dominant cloud service providers, Microsoft and Google in Europe. The dominant position obtained by these providers means that two aspects in governance of this socio-technical system in Europe must be attended to if Europe wants to regain control over their data and infrastructures: (1) European regulation focus needs to shift and (2) awareness must be raised at managerial level in enterprises.