Publications de Florian Tramèr

Advances and Open Problems in Federated Learning

Ayfer Özgür Aydin, Florian Tramèr, Qiang Yang, Ananda Theertha Suresh, Badih Ghazi, Lie He, Martin Jaggi, Sebastian Urban Stich, Yang Liu

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

NOW PUBLISHERS INC2021

Addressing Beacon Re-Identification Attacks: Quantification and Mitigation of Privacy Risks

Jean-Pierre Hubaux, Shuang Wang, Florian Tramèr, Jean Louis Raisaro, Carlos Bustamante

The Global Alliance for Genomics and Health (GA4GH) created the Beacon Project as a means of testing the willingness of data holders to share genetic data in the simplest technical context query for the presence of a specified nucleotide at a given positio ...

Oxford Univ Press2017

PrivateRide: A Privacy-Enhanced Ride-Hailing Service

Jean-Pierre Hubaux, Kévin Clément Huguenin, Florian Tramèr, Bastien Jacot-Guillarmod, Thi Van Anh Pham, Taha Hajar, Italo Ivan Dacosta Petrocelli

In the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise sig ...

2017

Stealing Machine Learning Models via Prediction APIs

Florian Tramèr, Fan Zhang, Ari Juels

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as- ...

Usenix Assoc2016

On solving LPN using BKW and variants Implementation and Analysis

Serge Vaudenay, Florian Tramèr, Sonia Mihaela Bogos

The Learning Parity with Noise problem (LPN) is appealing in cryptography as it is considered to remain hard in the post-quantum world. It is also a good candidate for lightweight devices due to its simplicity. In this paper we provide a comprehensive anal ...

Springer2016

Better Algorithms for LWE and LWR

Serge Vaudenay, Alexandre Raphaël Duc, Florian Tramèr

The Learning With Error problem (LWE) is becoming more and more used in cryptography, for instance, in the design of some fully homomorphic encryption schemes. It is thus of primordial importance to find the best algorithms that might solve this problem so ...

Springer2015

Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

Jean-Pierre Hubaux, Florian Tramèr, Erman Ayday, Zhicong Huang

Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential priva ...

2015