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.

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.

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.