Designing digital services with cryptographic guarantees for data security and privacy

In the past decades, tremendously successful digital services have been built that collect, process, and monetize massive amounts of personal user data. Along come serious threats to data security and privacy. We identify Secure Multiparty Computation (SMC) as a rigorous approach to provide data security and privacy protection, but notice that its potential is foremost on the theoretical level. In order to bridge the gap between theory and real-world applications, we identify three research challenges: i) Extending the functionality and ii) increasing the efficiency of SMC as well as iii) customizing it to challenged environments. We choose a use case-driven research methodology which allows us to motivate and validate all our contributions in practice. First, we motivate the problem of financial privacy in cryptocurrencies and propose decentralized mixing secured via SMC as a solution. Second, we propose efficient SMC designs for different classification algorithms to address data security and privacy issues in machine learning. Finally, we investigate secure outsourcing as a general strategy to customize SMC to challenged deployment and operation scenarios by the example of computing set intersections, a fundamental SMC problem. In summary, the contributions made in this thesis widen the technical solution space for practical data security and privacy protection in data-driven digital services.