All projects Privacy Protection & CryptographyBlockchains & Smart ContractsSoftware VerificationDevice & System SecurityMachine LearningFinanceHealthGovernment & HumanitarianCritical InfrastructureDigital Information
Sep 2019 → Nov 2021 Project

Analysis of encryption techniques in ACARS communications

Partner: armasuisse
Partner contact: Martin Strohmeier
EPFL laboratory: Security and Privacy Engineering Laboratory (SPRING)
EPFL contact: Prof. Carmela Troncoso, Wouter Lueks

In this collaboration (structured in two projects) we develop an automated tool to flag messages sent by planes which are suspicious of using weak encryption mechanisms. We mainly focus on detecting the use of classical ciphers like substitution and transposition ciphers. The tool flags messages and identifies the family of ciphers. We also aim to develop automated decryption techniques for the weakest ciphers.

TopicsPrivacy Protection & CryptographyCritical Infrastructure

Jul 2019 → Dec 2021 Project

Deep Learning, Jumps, and Volatility Bursts

Partner: Swissquote
Partner contact: Serge Kassibrakis
EPFL laboratory: Swiss Finance Institute @ EPFL
EPFL contact: Prof. Damir Filipovic, Alexis Marchal

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.

TopicsMachine LearningFinance

Apr 2019 → Apr 2020 Project

Auditable Sharing and Management of Sensitive Data Across Jurisdictions

Partner: Swiss Re
Partner contact: Stephan Schreckenberg
EPFL laboratory: Decentralized Distributed Systems Laboratory (DEDIS)
EPFL contact: Prof. Bryan Ford

This work aims at creating a Proof of Concept of storing and managing data on a blockchain. This work answers the following two use-cases: (i) compliant storage, transfer and access management of (personal) sensitive data and (ii) compliant cross-border or cross-jurisdiction data sharing. DEDIS brings to the table a permissioned blockchain and distributed ledger using a fast catch up mechanism that allows for very fast processing of the requests, while staying secure. It also includes a novel approach to encryption and decryption, where no central point of failure can let the documents be published to outsiders (Calypso). Swiss Re brings to the table interesting use cases which will require DEDIS to extend Calypso to implement data location policies.

TopicsPrivacy Protection & CryptographyBlockchains & Smart ContractsSoftware Verification

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