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The microservice architectural style has gained much attention from both academia and industry recently as a novel way to design, develop, and deploy cloud-native applications. This concept encourages the decomposition of a monolith into multiple independently deployable units. A typical microservices-based application is formed of two service types: functional services, which provide the core business logic, and infrastructure services, which provide essential functionalities for a microservices ecosystem. To improve developers' productivity, many software frameworks have been developed to provide those reusable infrastructure services, allowing programmers to focus on implementing microservices in arbitrary ways. In this work, we made use of four open source frameworks to develop a cloud-based application in order to compare and evaluate their usability and practicability. While all selected frameworks promote asynchronous microservice design in general, there are differences in the ways each implements services. This leads to interoperability issues, such as message topic naming convention. Additionally, a key finding is the long startup times of JVM-based services that might reduce application's resiliency and portability. Some other advantages come directly from the programming language, such as the ability of Go to generate native binary executables, which results in very small and compact Docker images (up to 78\% smaller compared to other languages).

This chapter gives a brief summary about the first part of the thesis and transitions to the second part of the thesis.

In this chapter, we present the system GroupMusic that allows the generation and playback of group music playlists that are based on the musical taste of individual guests attending a meeting. In our architecture, we use mobile sensing to unobtrusively collect musical taste on smartphones for the automatized generation of group music playlists. We follow the idea of utilizing context data in a preprocessing step to generate a group music profile for the recommendation process that generates a group music playlist. The whole process is almost fully automated and the played back music automatically adapts to the users currently present. The results are relevant for researchers and developers in the fields of ubiquitous systems and group recommender systems.

Authentication with username and password is becoming an inconvenient process for the user. End users typically have little control over their personal privacy, and data breaches effecting millions of users have already happened several times. We have implemented a proof of concept decentralized OpenID Connect Provider by marrying it with Self-Sovereign Identity, which gives users the freedom to choose from a very large pool of identity providers instead of just a select few corporations, thus enabling the democratization of the highly centralized digital identity landscape. Furthermore, we propose a verifiable credential powered decentralized Public Key Infrastructure using distributed ledger technologies, which creates a straightforward and verifiable way for retrieving digital certificates., Comment: Accepted for publication at the 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS 2020)

In the field of social networking services, finding similar users based on profile data is common practice. Smart-phones harbor sensor and personal context data that can be used for user profiling. Yet, one vast source of personal data, that is text messaging data, has hardly been studied for user profiling. We see three reasons for this: First, private text messaging data is not shared due to their intimate character. Second, the definition of an appropriate privacy-preserving similarity measure is non-trivial. Third, assessing the quality of a similarity measure on text messaging data representing a potentially infinite set of topics is non-trivial. In order to overcome these obstacles we propose affinity, a system that assesses the similarity between text messaging histories of users reliably and efficiently in a privacy-preserving manner. Private texting data stays on user devices and data for comparison is compared in a latent format that neither allows to reconstruct the comparison words nor any original private plain text. We evaluate our approach by calculating similarities between Twitter histories of 60 US senators. The resulting similarity network reaches an average 85.0% accuracy on a political party classification task.

In this experience paper, we summarize the analysis of key technologies that are prerequisite for a secure realtime communication and computing Infrastructure in the smart factory, which supports flexible and reconfigurable production assets with a real-time digital representation. A broad range of Industry 4.0 use cases is evaluated – jointly with industrial application partners - and a set of basic requirements is derived from them. These challenges need to be addressed by available and upcoming ICT technologies in the domains of cloud computing in an industrial environment, virtualization and industrial Edge Computing, 5G radio and network, analytics with big and fast data, and Artificial Intelligence / Machine Learning technologies. The analysis includes mechanisms for secure and reliable connectivity in production, secure wireless communication and secure processes, massive sensor data analysis, and (virtual) network elements like secure gateway. Challenges and opportunities for new applications in production are described in the following.

International audience; Cross-domain identity management remains a major challenge for potential WebRTC adopters. In order to provide a global web-based communication system, it is critical to locate the destination called party, map the identity to the user device, and provide mutual authentication for both caller and called party. In this paper, we present a novel identity management and user discovery framework that enables callers to search and locate users across service domains. The identity management is decoupled from the used calling service, allowing users to manage their profiles and credentials independently of the applications. The framework is designed to preserve privacy and exploit web technology to gain trust and contact list management.

Two of the most popular applications of smartphones are online social networking and playing back music. In this paper, we present the design and implementation of a prototype that combines those usages by creating an architecture that allows the generation and playback of group music playlists that are based on the musical taste of individual guests attending a meeting. In our architecture, we utilize automatically collected data on smartphones for the automatized generation of group music playlists. We follow the idea of utilizing context data in a preprocessing step to generate a group music profile for the recommendation process that generates a group music playlist. For designing such an architecture, we consider current discussions on privacy, data ownership, and data control.

As of today, communication habits are shifting towards Online Social Network (OSN) services such as WhatsApp or Facebook. Still, OSN platforms are mostly built in a closed, proprietary manner that disallows users from communicating seamlessly between different OSN services. These lock-in effects are used to discourage users to migrate to other services. To overcome the obvious drawbacks of proprietary protocols and service architectures, SONIC proposes a holistic approach that facilitates seamless connectivity between different OSN platforms and allows user accounts to be migrated between OSN platforms without losing data or connections to other user profiles. Thus, SONIC builds the foundation for an open and heterogeneous Online Social Network Federation (OSNF). In this paper, we present a distributed and domain-independent ID management architecture for the SONIC OSNF, which allows user identifiers (GlobalID) to remain unchanged even when a profile is migrated to a different OSN platform. In order to resolve a given GlobalID to the actual URL of a social profile the Global Social Lookup System (GSLS), a distributed directory service built on peer to peer technology is introduced. Datasets called Social Records, which comprise all information required to look up a certain profile, are stored and published by the GSLS. Following this approach, social profiles can be migrated between OSN platforms without changing the user identifier, or losing connections to other users’ social profiles.

With the spread of fast mobile Internet connections, such as 3G and LTE and the increasing processor power of mobile devices accessing cloud computing services on-the-go is common among all users. Sharing private information with friends and family members are options of popular cloud services, such as storage and social media services. But recent headlines show that the access to private information is often not sufficiently secured on the service level. The approach presented in this paper aims to use attribute-based meta-information to secure data on the level of files without relying on additional functionality of third-party services. A mobile device app is used to access and alter the meta-information. Attribute-based encryption mechanisms secure the private data and define access policies for friends and other users simultaneously.