Projects

Jan 2022Dec 2023

Status:Ongoing

Invariant Federated Learning: Decentralized Training of Robust Privacy-Preserving Models

Partner: Microsoft

Partner contact: Dimitrios Dimitriadis, Emre Kıcıman, Robert Sim, Shruti Tople

EPFL laboratory: Data Science Lab (dlab)

EPFL contact: Prof. Robert West, Valentin Hartmann, Maxime Peyrard

As machine learning (ML) models are becoming more complex, there has been a growing interest in making use of decentrally generated data (e.g., from smartphones) and in pooling data from many actors. At the same time, however, privacy concerns about organizations collecting data have risen. As an additional challenge, decentrally generated data is often highly heterogeneous, thus breaking assumptions needed by standard ML models. Here, we propose to “kill two birds with one stone” by developing Invariant Federated Learning, a framework for training ML models without directly collecting data, while not only being robust to, but even benefiting from, heterogeneous data.

Topics: Machine Learning
Jan 2022Dec 2023

Status:Ongoing

Tyche: Confidential Computing on Yesterday’s Hardware

Partner: Microsoft

Partner contact: Adrien Ghosn, Marios Kogias

EPFL laboratory: Data Center Systems Laboratory (DCSL) , HexHive Laboratory

EPFL contact: Prof. Edouard Bugnion, Prof. Mathias Payer

Confidential computing is an increasingly popular means to wider Cloud adoption. By offering confidential virtual machines and enclaves, Cloud service providers now host organizations, such as banks and hospitals, that abide by stringent legal requirement with regards to their client’s data confidentiality. Unfortunately, confidential computing solutions depend on bleeding-edge emerging hardware that (1) takes long to roll out at the Cloud scale and (2) as a recent technology, it is bound to frequent changes and potential security vulnerabilities. This proposal leverage existing commodity hardware combined with new programming language and formal method techniques and identify how to provide similar or even more elaborate confidentiality and integrity guarantees than the existing confidential hardware.

Topics: Privacy Protection & Cryptography
Jan 2022Dec 2023

Status:Ongoing

PAIDIT: Private Anonymous Identity for Digital Transfers

Partner: ICRC, funded by HAC

Partner contact: TBD

EPFL laboratory: Decentralized Distributed Systems Laboratory (DEDIS)

EPFL contact: Prof. Bryan Ford

To serve the 80 million forcibly-displaced people around the globe, direct cash assistance is gaining acceptance. ICRC’s beneficiaries often do not have, or do not want, the ATM cards or mobile wallets normally used to spend or withdraw cash digitally, because issuers would subject them to privacy-invasive identity verification and potential screening against sanctions and counterterrorism watchlists. On top of that, existing solutions increase the risk of data leaks or surveillance induced by the many third parties having access to the data generated in the transactions. The proposed research focuses on the identity, account, and wallet management challenges in the design of a humanitarian cryptocurrency or token intended to address the above problems.

Topics: Privacy Protection & Cryptography, Blockchains & Smart Contracts, Device & System Security, Finance, Government & Humanitarian
Oct 2021Oct 2023

Status:Ongoing

RuralUS: Ultrasound adapted to resource limited settings

Partner: ICRC, CHUV

Partner contact: Mary-Anne Hartley

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley

Point-of-Care Ultrasound (PoCUS) is a powerfully versatile and virtually consumable-free clinical tool for the diagnosis and management of a range of diseases. While the promise of this tool in resource-limited settings may seem obvious, it’s implementation is limited by inter-user bias, requiring specific training and standardisation.This makes PoCUS a good candidate for computer-aided interpretation support. Our study proposes the development of a PoCUS training program adapted to resource limited settings and the particular needs of the ICRC.

Topics: Machine Learning, Health
Oct 2020Sep 2023

Status:Ongoing

Multi-Task Learning for Customer Understanding

Partner: Swisscom

Partner contact: Dan-Cristian Tomozei

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard, Nikolaos Dimitriadis

Customer understanding is a ubiquitous and multifaceted business application whose mission lies in providing better experiences to customers by recognising their needs. A multitude of tasks, ranging from churn prediction to accepting upselling recommendations, fall under this umbrella. Common approaches model each task separately and neglect the common structure some tasks may share. The purpose of this project is to leverage multi-task learning to better understand the behaviour of customers by modeling similar tasks into a single model. This multi-objective approach utilises the information of all involved tasks to generate a common embedding that can be beneficial to all and provide insights into the connection between different user behaviours, i.e. tasks. The project will provide data-driven insights into customer needs leading to retention as well as revenue maximisation while providing a better user experience.

Topics: Machine Learning, Digital Information
May 2021May 2023

Status:Ongoing

Harmful Information Against Humanitarian Organizations

Partner: ICRC, funded by HAC

Partner contact: Fabrice Lauper

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Rebekah Overdorf

In this project, we are working with the ICRC to develop technical methods to combat social media-based attacks against humanitarian organizations. We are uncovering how the phenomenon of weaponizing information impacts humanitarian organizations and developing methods to detect and prevent such attacks, primarily via natural language processing and machine learning methods.

Topics: Machine Learning, Government & Humanitarian
Apr 2022Mar 2023

Status:Ongoing

Adversarial Attacks in Neural Machine Translation Systems

Partner: Cyber-Defence Campus (armasuisse)

Partner contact: Ljiljana Dolamic

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

Recently, deep neural networks have been applied in many different domains due to their significant performance. However, it has been shown that these models are highly vulnerable to adversarial examples. Adversarial examples are slightly different from the original input but can mislead the target model to generate wrong outputs. Various methods have been proposed to craft these examples in image data. However, these methods are not readily applicable to Natural Language Processing (NLP). In this project, we aim to propose methods to generate adversarial examples for NLP models such as neural machine translation models in different languages. Moreover, through adversarial attacks, we mean to analyze the vulnerability and interpretability of these models.

Topics: Device & System Security, Machine Learning
Mar 2022Feb 2023

Status:Ongoing

ARNO

Partner: armasuisse

Partner contact: Gérôme Bovet

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

State-of-the-art architectures for modulation recognition are typically based on deep learning models. However, recently these models have been shown to be quite vulnerable to very small and carefully crafted perturbations, which pose serious questions in terms of safety, security, or performance guarantees at large. While adversarial training can improve the robustness of the network, there is still a large gap between the performance of the model against clean and perturbed samples. Based on recent experiments, the data used during training could be an important factor in the susceptibility of the models. Thus, the objective of this project is to research the effects of proper data selection, cleaning and preprocessing of the samples used during training on robustness.

Topics: Device & System Security, Machine Learning
Feb 2021Feb 2023

Status:Ongoing

PriBAD: Private Biometrics for Aid Distribution

Partner: ICRC, funded by HAC

Partner contact: Vincent Graf

EPFL laboratory: Security and Privacy Engineering Laboratory (SPRING)

EPFL contact: Prof. Carmela Troncoso, Wouter Lueks

In this project, we work on providing a privacy-preserving biometric solution for humanitarian aid distribution. The project seeks to understand the requirements of aid distribution in emergency situation and design a solution that enables the use of biometrics without endangering the beneficiaries that need access to aid.

Topics: Privacy Protection & Cryptography, Government & Humanitarian
Jan 2021Dec 2022

What If….? Pandemic Policy Decision Support System

Partner: Swiss RE

Partner contact: Mary-Anne Hartley

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley, Prof. Martin Jaggi, Prakhar Gupta, Giorgio Mannarini, Francesco Posa

After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiology as well as the volatile distribution of socioeconomic and cultural factors. This study proposes a data-driven approach to quantify the efficacy of the type, duration, and stringency of COVID-19 mitigation policies in terms of transmission control and economic loss, personalised to individual countries.

Topics: Machine Learning, Health, Government & Humanitarian
Mar 2022Nov 2022

Technology Monitoring and Management (TMM)

Partner: armasuisse

Partner contact: Alain Mermoud

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Angelika Romanou

The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.

Topics: Machine Learning
Apr 2021Mar 2022

Adversarial Attacks in Natural Language Processing Systems

Partner: Cyber-Defence Campus (armasuisse)

Partner contact: Ljiljana Dolamic

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard, Sahar Sadrizadeh

Recently, deep neural networks have been applied in many different domains due to their significant performance. However, it has been shown that these models are highly vulnerable to adversarial examples. Adversarial examples are slightly different from the original input but can mislead the target model to generate wrong outputs. Various methods have been proposed to craft these examples in image data. However, these methods are not readily applicable to Natural Language Processing (NLP). In this project, we aim to propose methods to generate adversarial examples for NLP models such as neural machine translation models in different languages. Moreover, through adversarial attacks, we mean to analyze the vulnerability and interpretability of these models.

Topics: Device & System Security, Machine Learning, Government & Humanitarian
Mar 2020Mar 2022

Technology Monitoring and Management (TMM)

Partner: armasuisse

Partner contact: Alain Mermoud

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Chi Thang Duong

The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.

Topics: Machine Learning
Mar 2021Feb 2022

ADAN

Partner: armasuisse

Partner contact: Gérôme Bovet

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

Modulation recognition state-of-the-art architectures use deep learning models. These models are vulnerable to adversarial perturbations, which are imperceptible additive noise crafted to induce misclassification, posing serious questions in terms of safety, security, or performance guarantees at large. One of the best ways to make the model robust is to use adversarial learning, in which the model is fine-tuned with these adversarial perturbations. However, this method has several drawbacks. It is computationally costly, has convergence instabilities and it does not protect against multiple types of corruptions at the same time. The objective of this project is to develop improved and effective adversarial training solutions that tackle these drawbacks.

Topics: Device & System Security, Machine Learning
Apr 2018Dec 2021

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).

Topics: Privacy Protection & Cryptography, Machine Learning, Health
Jul 2019Dec 2021

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.

Topics: Machine Learning, Finance
Nov 2019Dec 2021

Deep Learning for Asset Bubbles Detection

Partner: Swissquote

Partner contact: Serge Kassibrakis

EPFL laboratory: Swiss Finance Institute @ EPFL

EPFL contact: Prof. Damir Filipovic, Alexis Marchal

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.

Topics: Machine Learning, Finance
Sep 2020Dec 2021

Risk & returns around FOMC press conferences: a novel perspective from computer vision

Partner: Swissquote

Partner contact: Serge Kassibrakis

EPFL laboratory: Swiss Finance Institute @ EPFL

EPFL contact: Prof. Damir Filipovic, Alexis Marchal

I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.

Topics: Machine Learning, Finance
Nov 2018Dec 2021

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.

Topics: Machine Learning, Government & Humanitarian
Jan 2019Dec 2021

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.

Topics: Digital Information