Projects
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.
Topics • Privacy Protection & Cryptography • Health
ViewTTL-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.
Topics • Digital Information
ViewMonitoring, 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.
Topics • Machine Learning • Health
ViewDigitalizing 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.
Topics • Machine Learning • Government & Humanitarian
ViewProduction-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.
Topics • Privacy Protection & Cryptography • Blockchains & Smart Contracts • Software Verification
ViewSafeAI
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.
Topics • Device & System Security • Machine Learning
ViewData 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).
Topics • Privacy Protection & Cryptography • Machine Learning • Health
View