All projects Privacy Protection & CryptographyBlockchains & Smart ContractsSoftware VerificationDevice & System SecurityMachine LearningFinanceHealthGovernment & HumanitarianCritical InfrastructureDigital Information
Mar 2019 → Mar 2020 Project

MedCo: Collective Protection of Medical Data

Partner: CHUV
Partner contact: Nicolas Rosat, Jean-Louis Raisaro
EPFL laboratory: Laboratory for Data Security (LDS)
EPFL contact: Prof. Jean-Pierre Hubaux

MedCo, developed in the LDS lab of professor Jean-Pierre Hubaux in collaboration with professor Bryan Ford’s DEDIS lab and the Lausanne University Hospital (CHUV), is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables hundreds of clinical sites to collectively protect their data and to securely share them with investigators, without single points of failure. MedCo applies advanced privacy-enhancing techniques, such as: Multi-party homomorphic encryption, Secure distributed protocols and Differential privacy.

TopicsPrivacy Protection & CryptographyHealth

Jan 2019 → Dec 2021 Project

TTL-MSR Taiming Tail-Latency for Microsecond-scale RPCs

Partner: Microsoft
Partner contact: Irene Zhang, Dan Ports, Marios Kogias
EPFL laboratory: Data Center Systems Laboratory (DCSL)
EPFL contact: Prof. Edouard Bugnion, Konstantinos Prasopoulos

We consider a web-scale application within a datacenter that comprises of hundreds of software components, deployed on thousands of servers. These versatile components communicate with each other via Remote Procedure Calls (RPCs) with the cost of an individual RPC service typically measured in microseconds. The end-user performance, availability and overall efficiency of the entire system are largely dependent on the efficient delivery and scheduling of these RPCs. We propose to make RPC first-class citizens of datacenter deployment. This requires a revisitation of the overall architecture, application API, and network protocols. We are also building the tools that are necessary to scientifically evaluate microsesecond-scale services.

TopicsDigital Information

Jan 2019 → Dec 2021 Project

Monitoring, Modelling, and Modifying Dietary Habits and Nutrition Based on Large-Scale Digital Traces

Partner: Microsoft
Partner contact: Ryen W. White
EPFL laboratory: Data Science Lab
EPFL contact: Prof. Robert West, Kristina Gligoric

The overall goal of this project is to develop methods for monitoring, modeling, and modifying dietary habits and nutrition based on large-scale digital traces. We will leverage data from both EPFL and Microsoft, to shed light on dietary habits from different angles and at different scales. Our agenda broadly decomposes into three sets of research questions: (1) Monitoring and modeling, (2) Quantifying and correcting biases and (3) Modifying dietary habits. Applications of our work will include new methods for conducting population nutrition monitoring, recommending better-personalized eating practices, optimizing food offerings, and minimizing food waste.

TopicsMachine LearningHealth

Nov 2018 → Dec 2021 Project

Digitalizing search for missing persons

Partner: CICR, FLO
Partner contact: Fabrice Lauper
EPFL laboratory: Distributed Information Systems Laboratory (LSIR)
EPFL contact: Prof. Karl Aberer, Rémi Lebret

Armed conflicts, violence and migration are causing large scale separation of family members, dislocation of family links and missing persons. People must receive help to know what happened to reconnect to their loved ones as rapidly as possible. The ICRC and LSIR through its partnership have set themselves a challenge to analyse publicly available data through analytics techniques to identify missing persons that would arguably not have been identified using current, conventional methods. The goal of this project is to facilitate the search for missing individuals by building scalable, accurate systems tailored for that purpose.

TopicsMachine LearningGovernment & Humanitarian

Nov 2018 → Oct 2019 Project

Production-Readiness Timeline for Skipchains with onChain secrets

Partner: ByzGen
Partner contact: Marcus Ralphs
EPFL laboratory: Decentralized Distributed Systems Laboratory (DEDIS)
EPFL contact: Prof. Bryan Ford

The DEDIS team created a first version of the onChain secrets implementation using its skipchain blockchain. This implementation allows a client to store encrypted documents on a public but permissioned blockchain and to change the access rights to those documents after they have been written to the blockchain. The first implementation has been extensively tested by ByzGen and is ready to be used in a PoC demo. This project aims at increasing its performance and stability, and make it production-ready. Further, it will add a more realistic testing platform that will allow to check the validity of new functionality in a real-world setting and find regressions before they are pushed to the stable repository.

TopicsPrivacy Protection & CryptographyBlockchains & Smart ContractsSoftware Verification

Jul 2018 → Oct 2018 Project


Partner: Cisco
Partner contact: Frank Michaud
EPFL laboratory: Signal Processing Laboratory (LTS4)
EPFL contact: Prof. Pascal Frossard, Apostolos Modas

SafeAI aims to develop cyber-security solutions in the context of Artificial Intelligence (AI). With the advent of generative AI, it is possible to attack AI enhanced applications with targeted cyberattacks, and also to generate cyberattacks that are automated and enhanced via the use of AI. The main goal of SafeAI is the development of a software that enables automated generation of adversarial attacks and defences using AI.

TopicsDevice & System SecurityMachine Learning

Apr 2018 → Dec 2021 Project

Data Protection in Personalized Health

Partner: CHUV, ETH
Partner contact: Prof. Jacques Fellay (EPFL/CHUV), Prof. Effy Vayena (ETH)
EPFL laboratory: Laboratory for Data Security (LDS)
EPFL contact: Prof. Jean-Pierre Hubaux

P4 (Predictive, Preventive, Personalized and Participatory) medicine is called to revolutionize healthcare by providing better diagnoses and targeted preventive and therapeutic measures. In order to enable effective P4 medicine, DPPH defines an optimal balance between usability, scalability and data protection, and develops required computing tools. The target result of the project will be a platform composed of software packages that seamlessly enable clinical and genomic data sharing and exploitation across a federation of medical institutions across Switzerland. The platform is scalable, secure, responsible and privacy-conscious. It can seamlessly integrate widespread cohort exploration tools (e.g., i2b2 and TranSMART).

TopicsPrivacy Protection & CryptographyMachine LearningHealth