Privacy-preserving Machine Learning and Privacy in Distributed Settings

Overview

Workshop 2 is part of the Data Privacy in Machine Learning P1 program. It will focus on privacy in limited-trust settings, with particular interest in approaches that bring together ideas from differential privacy, decentralized learning, secure computation, and cryptography.

The program includes invited talks by Christoph Lampert, Christian Rechberger, Edwige Cyffers, Christian Weinert, and Tamer Mour, as well as contributed talks selected through the call for contributions. The first day of the workshop will be a day of tutorials on differentially private machine learning and cryptography.

Registration

Registration deadline: October 28, 2026

Cost: 675 DKK

Register for the workshop

Speakers

Christoph Lampert

Professor, Institute of Science and Technology Austria

Christian Rechberger

Professor, TU Graz

Edwige Cyffers

CNRS Researcher, Lamsade at Dauphine University in Paris

Christian Weinert

Associate Professor, Royal Holloway, University of London

Tamer Mour

Principal Investigator, The Italian Institute of Artificial Intelligence (AI4I)

Call for Contributions

We are seeking submissions for 20-30 minute talks on topics related to privacy-preserving machine learning and privacy in distributed settings. The workshop will not have a poster session.

Topics of interest include:

• Privacy for decentralized and distributed learning
• Private federated learning, federated analytics, and secure aggregation
• Interactions between cryptographic tools and differential privacy
• Secure multi-party computation for privacy-preserving machine learning
• Zero-knowledge proofs and other methods for verifying privacy-preserving machine learning systems
• Attacks, auditing, and empirical evaluation of privacy
• Any other topic related to the core theme of the workshop

Preliminary Agenda

Venue

Organizers

Hannah Keller

Claudio Orlandi

Rasmus Pagh

Amartya Sanyal