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Trustworthy Information Processing

Today's Internet can be seen as a huge data store that collects personal and sensitive data about its users. This leads to significant security and privacy risks for end users, who lose control over the data they share. Developing methods and tools to enable a secure and privacy-friendly processing of data thus constitutes a core challenge to all data-driven ecosystems and applications. In particular, the success of digitalization heavily depends on whether companies are able to gain their users' trust regarding the protection of their privacy. This research area strives to develop disruptive new frameworks for reasoning about and improving security and privacy in information processing in various settings, efficiently and at scale. In the last years, this area had a particular focus on the following topics: novel methods and tools for the algorithmic sanitization of privacy-sensitive data, in particular for genomic and medical research; new techniques for quantitatively assessing end user privacy; as well as efficient techniques for secure, verifiable computation.

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Tagged Publications

Year 2026

Conference / Medium

Conference on Neural Information Processing Systems (NeurIPS)
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates

Conference / Medium

European Conference on Computer Vision (ECCV)
Data Circuit Breaker: Identifying Training, Test, and Generated Data in Image Generative Models

Conference / Medium

ACM International Conference on Knowledge Discovery and Data Mining (KDD)
Differentiably Discovering Sets of Rules

Conference / Medium

International Workshop on Designing and Measuring Security in Systems with AI (DeMeSSAI 2026)
MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

Conference / Medium

International Conference on Machine Learning Workshop(ICML- W)
HORST: Composing Optimizer Geometries for Sparse Transformer Training