The Secure, Reliable, and Intelligent Systems Lab (SRI) is a research group in the Department of Computer Science at ETH Zurich. Our current research focus is on the areas of reliable, secure, robust and fair machine learning, probabilistic and quantum programming, and machine learning for code. Our work led to three ETH spin-offs: (AI for Code), ChainSecurity (security verification), and LatticeFlow (robust machine learning). Please see Research and Publications to learn more.

Latest Blog Posts

Latest News

Latest News & Blog Posts

The Role of Red Teaming in PETs: In February, our team won the Red Teaming category of the U.S. PETs Prize Challenge, securing a prize of 60,000 USD. In this blog post, we will provide a brief overview of the significance of Red Teaming in the field of Privacy Enhancing Technologies (PETs) research in the context of the competition.

Timon Gehr, former doctoral student and current postdoctoral researcher at SRI Lab, has won the ETH Medal for his outstanding doctoral thesis. See website of D-INFK.

LAMP: Extracting text from gradients with language model priors: In this work we present an attack on federated learning's privacy specific to the text domain. We show that federated learning in the text domain can expose a lot of user data.

7-8 October 2022: Workshop on Dependable and Secure Software Systems, hosting leading scientists who will present the latest research and most advanced methods for addressing this fundamental challenge. Website

Reliability guarantees on private data: We present Phoenix (CCS '22), the first system for privacy-preserving neural network inference with robustness and fairness guarantees.

Professor Martin Vechev was appointed Full Professor of Computer Science in the Department of Computer Science. His achievements in a number of areas are globally regarded as groundbreaking.

Why tighter convex relaxations harm certified training: We investigate a long-standing paradox in the field of certified training, identifying previously overlooked properties of convex relaxations which affect training success.

Our new ETH spin-off LatticeFlow raises USD 2.8M to help companies build and deploy trustworty AI. Read articles on TechCrunch and ETH.