Christoph Lampert
Professor, Institute of Science and Technology Austria
Workshop 2
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
Speakers
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
Registration formRegister for the workshop by October 28, 2026
Preliminary Agenda
Tutorial: Primer on Differentially Private Machine Learning IHannah Keller
Coffee break
Tutorial: Primer on Differentially Private Machine Learning IITamalika Mukherjee
Lunch break
Tutorial: Cryptography Primer ITamer Mour
Coffee break
Tutorial: Cryptography Primer II
Invited TalkEdwige Cyffers
Coffee break
Contributed TalksDithered Gaussian Mechanism for Randomness-Efficient Differential PrivacyNikita Kalinin, Rasmus Pagh
Lower Bounds for Private Graph Optimization Problems using Reconstruction AttacksJacob Imola, Rasmus Pagh, Lukas Retschmeier
Edit-Neighboring Data Streams and Privacy under Continual ObservationJoel Daniel Andersson, Anamay Chaturvedi, Monika Henzinger, Roodabeh Safavi
Lunch break
Invited TalkRecent Advances in MPC-based Secure Neural Network InferenceChristian Weinert
Efficient secure neural network inference (SNNI) has been extensively studied over the past 10 years. However, recent foundational improvements have stagnated in favour of exploring the intricacies of transformer architectures. In this talk, we revisit SNNI in the context of emerging real-world deployment scenarios, including settings in which the model owner, or even all parties, know the model parameters in the clear. We show how a combination of improved function-dependent preprocessing, new MPC primitives, aggressive ML/MPC co-design, and implementation-level optimisations can substantially improve performance. Together, these techniques enable privacy-preserving evaluation of ResNet50 on ImageNet samples with amortized online runtimes below 0.1 seconds.
Contributed Talks
Accelerating Multiparty Noise Generation Using LookupsFredrik Meisingseth, Christian Rechberger, Fabian Schmid
Secret Shared Floating Point Computation for Private Neural Network Fine-TuningHendrik Eerikson, Sona Kravtšenko, Hiroki Kaminaga, Pille Pullonen-Raudvere, Liina Kamm, Sven Laur
Binarized Neural Network Inference with Half-Authenticated TriplesSebastian Hasler
Coffee break
Invited TalkTamer Mour
Invited TalkChristoph Lampert
Coffee break
Contributed Talks
When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated LearningWenrui Yu, Changlong Ji, Johannes Bjerva, Qiongxiu Li
Towards Practical vFHE: Privacy-preserving and Verifiable Machine Learning Ignacio Cascudo, Anamaria Costache, Daniele Cozzo, Dario Fiore, Antonio Guimaraes, Eduardo Soria-Vazquez
Encrypted neural networks without overflowsPhilipp Kern, Lorenzo Rovida, Samuel Teuber, Edoardo Manino, Carsten Sinz, Alberto Leporati
Lunch break
Invited TalkChristian Rechberger
Venue
Location: Aarhus, Denmark
More details to come
Organizers
Hannah Keller
Claudio Orlandi
Rasmus Pagh
