P1 Programs Workshop: Privacy-preserving Machine Learning and Privacy in Distributed Settings

Workshop 2

Privacy-preserving Machine Learning and Privacy in Distributed Settings

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.

Find more information here.

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
  
  

Important Dates & Submission Info:

• Submission Deadline: September 10, 2026
• Decisions Sent: September 17, 2026
• Link to Submission Form

Data Privacy in Machine Learning

The workshop is organised by the P1 Program on Data Privacy in Machine Learning, directed by Amartya Sanyal, Claudio Orlandi, and Rasmus Pagh.