The seminar explores Private Database Systems with an emphasis on Secure Collaborative Analysis (SCA) using Secure Multi-Party Computation (MPC). The seminar bridges modern database systems and applied cryptography, examining how analytical queries can be executed across mutually distrusting parties without revealing raw data.

The seminar progresses from fundamentals to research-level topics. Students will first understand the required cryptographic primitives (e.g., MPC protocols) and how standard relational query operators (e.g., selection, projection, joins, aggregation) are mapped into MPC primitives. From there, the seminar advances toward query optimization techniques tailored for MPC execution, including cost modeling and query planning.

The format is research-driven: students will read, present, and review key papers in the field. The seminar emphasizes deep technical understanding, analytical thinking, and the ability to evaluate research contributions. For the first part of the seminar, the instructor will present the cryptographic primitives and lead the discussion. In the second part of the seminar, students will take the lead in presenting, reviewing state-of-the-art MPC-based SCA frameworks, and discussing their benefits/drawbacks in practice.

This seminar is valuable for students aiming for careers at the intersection of databases and security. Understanding how database queries are executed and optimized under secure multi-party computation is essential for building scalable, trustworthy collaborative data systems in an increasingly privacy-constrained world.

Below, you find some general information about the seminar. For all information regarding this year’s seminar (including important dates), please check the Moodle course linked above. Also make sure that you are registered in TUCaN.

Prerequisites:

You should have basic knowledge in machine learning and programming in Python. Advanced knowledge in data management and database systems from courses such as SDMS or ADMS as well as machine learning courses is also helpful.

Seminar Topic:

Database management systems (DBMS) in the cloud are the backbone for managing large volumes of data efficiently and thus play a central role in business and science today. For providing high performance, many of the most complex DBMS components such as query optimizers or schedulers involve solving non-trivial problems.

To tackle such problems, very recent work has outlined a new direction of so-called learned DBMS components where AI-based methods are used to replace and enhance core DBMS components, which has been shown to provide significant performance benefits. This route is particularly interesting since Cloud vendors such as Google, Amazon, and Microsoft are already applying these techniques to optimize the performance of their cloud data systems.

Besides learned DBMS components, AI has been used to improve many other data management-related tasks. For example, classical data engineering tasks like error detection, missing value imputation, and data augmentation typically cause high manual overheads and can be automated with AI. Finally, AI has also been used to extend databases through better data access interfaces (e.g., natural language querying and chatbots for data) or by supporting data beyond structured tabular data (i.e., text and images).

This seminar is designed to introduce students to the foundational concepts of using AI for data management. The course will include a mini lecture series that provides the necessary background on AI in data management, preparing students for the seminar tasks. The seminar is divided into two parts, each focusing on key themes as introduced above: learned DBMS components and the application of AI for data engineering. Students will engage in practical tasks related to these topics.